diff --git a/demo/dist/metacar.min.js b/demo/dist/metacar.min.js index c8f1cf2..d4e7f7c 100644 --- a/demo/dist/metacar.min.js +++ b/demo/dist/metacar.min.js @@ -1 +1 @@ -window.metacar=function(t){var e={};function i(o){if(e[o])return e[o].exports;var n=e[o]={i:o,l:!1,exports:{}};return t[o].call(n.exports,n,n.exports,i),n.l=!0,n.exports}return i.m=t,i.c=e,i.d=function(t,e,o){i.o(t,e)||Object.defineProperty(t,e,{enumerable:!0,get:o})},i.r=function(t){"undefined"!=typeof Symbol&&Symbol.toStringTag&&Object.defineProperty(t,Symbol.toStringTag,{value:"Module"}),Object.defineProperty(t,"__esModule",{value:!0})},i.t=function(t,e){if(1&e&&(t=i(t)),8&e)return t;if(4&e&&"object"==typeof t&&t&&t.__esModule)return t;var o=Object.create(null);if(i.r(o),Object.defineProperty(o,"default",{enumerable:!0,value:t}),2&e&&"string"!=typeof t)for(var n in t)i.d(o,n,function(e){return t[e]}.bind(null,n));return o},i.n=function(t){var e=t&&t.__esModule?function(){return t.default}:function(){return t};return i.d(e,"a",e),e},i.o=function(t,e){return Object.prototype.hasOwnProperty.call(t,e)},i.p="",i(i.s=1)}([function(t,e){t.exports=window.PIXI},function(t,e,i){"use strict";i.r(e);var o=i(0),n=(o.Application,o.loader),a=(o.loader.resources,o.Sprite),r=o.Graphics,s=o.Container,h="https://metacar-project.com/",c=h+"public/textures/textures.json",l=h+"public/images/",d=60,p={DEFAULT:"car.png",AGENT:"car_agent.png"},u={ROAD:-1,DEFAULT:0,CAR:1,AGENT:1},v={ROADS:{"↕":{image:"road_u.png",orientation:1.5},"↔":{image:"road_r.png",orientation:0},"↱":{image:"road_rotate_ld.png",orientation:1.25},"↰":{image:"road_rotate_ul.png",orientation:.75},"↲":{image:"road_rotate_rl.png",orientation:.25},"↳":{image:"road_rotate_dl.png",orientation:1.75},"↟":{image:"road_up.png",orientation:1.5},"↠":{image:"road_rp.png",orientation:0},"↤":{image:"road_r2.png",orientation:0},"↦":{image:"road_l2.png",orientation:1},"↥":{image:"road_d2.png",orientation:.5},"↧":{image:"road_u2.png",orientation:1.5}},house:{image:"house.png"},house2:{image:"house2.png"},house3:{image:"house3.png"},bench:{image:"bench.png"},tree:{image:"tree.png"}},y={level:{fullCity:{cars:[{mx:2,my:5,line:0,auto:!0},{mx:3,my:4,line:0,auto:!0},{mx:3,my:4,line:1,auto:!0},{mx:6,my:3,line:0,auto:!0},{mx:6,my:3,line:1,auto:!0},{mx:8,my:2,line:0,auto:!0},{mx:8,my:2,line:1,auto:!0},{mx:2,my:7,line:0,auto:!0}],house:[{x:9,y:409},{x:146,y:162},{x:439,y:39}],house2:[{x:425,y:192},{x:431,y:354}],house3:[{x:426,y:281},{x:51,y:157},{x:553,y:34}],bench:[{x:259,y:206},{x:8,y:205}],tree:[{x:1,y:303},{x:54,y:357},{x:215,y:325},{x:230,y:400},{x:282,y:368},{x:182,y:433},{x:285,y:309},{x:576,y:233},{x:581,y:300},{x:519,y:388},{x:582,y:391},{x:256,y:69},{x:151,y:43},{x:77,y:77},{x:26,y:24},{x:189,y:98}],map:[[0,0,0,0,0,0,"↕",0,0,0,0],[0,0,0,0,0,0,"↟",0,0,0,0],[0,0,0,0,0,0,"↦","↔","↔","↠","↔"],[0,0,0,0,0,0,"↕",0,0,0,0],["↔","↔","↧","↔","↠","↔","↤",0,0,0,0],[0,0,"↕",0,0,0,"↕",0,0,0,0],[0,0,"↟",0,0,0,"↟",0,0,0,0],[0,0,"↕",0,0,0,"↕",0,0,0,0]]},level1:{cars:[{mx:5,my:3,line:0,auto:!0},{mx:5,my:3,line:1,auto:!0},{mx:5,my:1,line:0,auto:!0}],tree:[{x:120,y:50},{x:86.5,y:85},{x:94.5,y:135},{x:30.5,y:128},{x:72.5,y:190},{x:23.5,y:232},{x:110.5,y:242},{x:43.5,y:33},{x:18.5,y:178},{x:305.5,y:125},{x:360.5,y:122}],asset:[{x:252.5,y:142},{x:86.5,y:85},{x:94.5,y:135},{x:30.5,y:128},{x:252.5,y:26},{x:195.5,y:0},{x:307.5,y:1},{x:72.5,y:190},{x:23.5,y:232},{x:110.5,y:242},{x:43.5,y:33},{x:35.5,y:185},{x:18.5,y:178},{x:289.5,y:126},{x:337.5,y:126},{x:305.5,y:125},{x:360.5,y:122},{x:492.5,y:56},{x:503.5,y:136},{x:504.5,y:58}],bench:[{x:252.5,y:142},{x:252.5,y:26}],road:[{x:240,y:60},{x:240,y:60},{x:480,y:0},{x:480,y:60},{x:480,y:120},{x:480,y:180},{x:480,y:240},{x:300,y:180},{x:300,y:180},{x:360,y:180},{x:420,y:180},{x:420,y:120},{x:420,y:60},{x:360,y:60},{x:300,y:60},{x:420,y:120}],house3:[{x:503.5,y:136}],house:[{x:504.5,y:58}],map:[[0,0,0,0,0,0,0,0,0,0],[0,0,0,"↱","↠","↔","↔","↰",0,0],[0,0,0,"↕",0,0,0,"↟",0,0],[0,0,0,"↳","↔","↔","↔","↲",0,0],[0,0,0,0,0,0,0,0,0,0]],agent:{mx:4,my:3,line:0,motion:{type:"BasicMotionEngine",options:{rotationStep:.5,actions:["UP","LEFT","RIGHT"]}}}},level0:{cars:[],house:[{x:263,y:108},{x:112,y:258}],house3:[{x:262,y:16}],bench:[{x:135,y:34}],tree:[{x:124,y:134},{x:7,y:11},{x:69,y:10},{x:19,y:238},{x:5,y:133},{x:260,y:211}],map:[[0,0,0,0,0],[0,"↱","↔","↰",0],[0,"↕",0,"↕",0],[0,"↳","↠","↲",0],[0,0,0,0,0]],agent:{mx:2,my:1,line:1,motion:{type:"BasicMotionEngine",options:{rotationStep:.5,actions:["UP","LEFT","RIGHT","DOWN","WAIT"]}}}},level2:{cars:[{mx:7,my:7,line:0},{mx:9,my:6,line:0},{mx:8,my:6,line:1},{mx:10,my:6,line:1}],house:[{x:68,y:317}],house2:[{x:624,y:84},{x:-15,y:144}],house3:[{x:618,y:231},{x:13,y:441}],bench:[{x:318,y:216},{x:422,y:36}],tree:[{x:151,y:144},{x:229,y:175},{x:326,y:132},{x:413,y:167},{x:487,y:157},{x:445,y:123},{x:511,y:429},{x:584,y:427},{x:80,y:391},{x:219,y:409},{x:207,y:326},{x:156,y:426},{x:162,y:13}],map:[[0,0,0,0,0,0,0,0,0,0,0],[0,"↱","↔","↔","↔","↔","↔","↔","↔","↰",0],[0,"↕",0,0,0,0,0,0,0,"↕",0],[0,"↕",0,0,0,0,0,0,0,"↕",0],[0,"↳","↔","↔","↔","↧","↔","↔","↔","↲",0],[0,0,0,0,0,"↕",0,0,0,0,0],[0,0,0,0,0,"↕",0,"↱","↔","↔","↔"],[0,0,0,0,0,"↕",0,"↕",0,0,0]],agent:{mx:2,my:4,line:0,motion:{type:"BasicMotionEngine",options:{rotationStep:.5,actions:["UP","LEFT","RIGHT","DOWN","WAIT"]}}}}}};function 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0,this.core.carId=C,C+=1,this.core.agent=!1,this.core.obstacle=!0,this.core.mapId=u.CAR,o.lidar&&(this.createLidar(o.lidarInfo),this.core.lidar=this.lidar),o.motionEngine?(this.motion=o.motionEngine,this.motion.setUp(this.core,this.lidar),this.core.rotationStep=.5):this.core.rotationStep=.5,this.core.mx=this.info.mx,this.core.my=this.info.my,this.core.x=this.info.mx*d,this.core.y=this.info.my*d,this.core.checkAndsetNewRoad=function(t){return n.checkAndsetNewRoad(t)};var r=this.level.getRoad(this.core.my,this.core.mx);this.core.line=0,r&&r.setCarPosition(this.core,this.info.line)}return t.prototype.checkAndsetNewRoad=function(t){var e=this.core.road;t||(t=this.level.getRoad(this.core.my,this.core.mx)),t!=e&&(e&&e.cars.splice(e.cars.indexOf(this.core.carId),1),this.core.road=t,this.core.haveTurned=!1,this.core.optionalTurn=!1,this.turnedRandom=void 0,t&&t.cars.push(this.core.carId))},t.prototype.reset=function(){this.core.mx=this.info.mx,this.core.my=this.info.my;var t=this.level.getRoad(this.core.my,this.core.mx);t&&t.setCarPosition(this.core,this.info.line,!0)},t.prototype.getState=function(t){if(void 0===t&&(t=!1),t){var e=[];return this.motion.state.map(function(t){e=e.concat(t)}),e}return this.motion.state.map(function(t){return t.slice()})},t.prototype.step=function(t,e){if(void 0===e&&(e=null),null==e)var i=this.motion.step(t),o=i.agentCollisions,n=i.onRoad;else{var a=this.motion.actionStep(t,e);o=a.agentCollisions,n=a.onRoad}return{agentCollisions:o,onRoad:n}},t.prototype.createLidar=function(t){t.pts=t.pts||5,t.width=t.width||4,t.height=t.height||5,t.pos=t.pos||0,this.lidar=new s,this.lidar.lidarPts=[],this.lidar.collisionPts=[];var e=new r;e.pt=!1,e.alpha=.3,e.beginFill(5329233),e.drawRect(0,0,this.core.width*t.width,this.core.height*t.height),e.endFill();var i=new r;i.pt=!1,i.alpha=0,i.beginFill(5308416),i.drawRect(this.core.width-5,e.height/2,3,3),i.endFill();var o=new r;o.pt=!1,o.alpha=0,o.beginFill(5308416),o.drawRect(-1,e.height/2-2,3,3),o.endFill();var n=new r;n.pt=!1,n.alpha=0,n.beginFill(5308416),n.drawRect(this.core.width/2,e.height/2-this.core.height/2+5,3,3),n.endFill();var a=new r;a.pt=!1,a.alpha=0,a.beginFill(5308416),a.drawRect(this.core.width/2,e.height/2+this.core.height/2-8,3,3),a.endFill(),o.x-=t.pos*this.core.width,i.x-=t.pos*this.core.width,a.x-=t.pos*this.core.width,n.x-=t.pos*this.core.width;for(var h=e.width/t.pts,c=e.height/t.pts,l=t.pts-1;l>=0;l--)for(var d=0;do.x&&i.xo.y&&i.y=4||!t)&&(r=!0),{agentCollisions:o,onRoad:r}},t.prototype.setState=function(t){for(var e=0,i=0;ithis.state[o][n]||t[a].mapId==u.ROAD&&this.state[o][n]==u.DEFAULT)&&(this.state[o][n]=t[a].mapId)}e+=1}},t}(),b=(I=Object.setPrototypeOf||{__proto__:[]}instanceof Array&&function(t,e){t.__proto__=e}||function(t,e){for(var i in e)e.hasOwnProperty(i)&&(t[i]=e[i])},function(t,e){function i(){this.constructor=t}I(t,e),t.prototype=null===e?Object.create(e):(i.prototype=e.prototype,new i)}),R=function(t){function e(e,i){var o=t.call(this,e)||this;return o.rotationStep=i.rotationStep||.5,o.actions=i.actions||["UP","LEFT","RIGHT","DOWN","WAIT"],o}return b(e,t),e.prototype.setUp=function(t,e){this.car=t,this.lidar=e,this.state=[];for(var i=0;i0&&(this.car.v=0,this.car.vy=0),{agentCollisions:i,onRoad:o}},e}(M),E=function(){var t=Object.setPrototypeOf||{__proto__:[]}instanceof Array&&function(t,e){t.__proto__=e}||function(t,e){for(var i in e)e.hasOwnProperty(i)&&(t[i]=e[i])};return function(e,i){function o(){this.constructor=e}t(e,i),e.prototype=null===i?Object.create(i):(o.prototype=i.prototype,new o)}}(),L=function(t){function e(e){var i=t.call(this,e)||this;return i.map=i.level.getMap(),i.mapSizeY=i.map.length,i.mapSizeX=i.map[0].length,i.rotationStep=.5,i.rotationToNextCase={},i.rotationToNextCase[0]={mx:1,my:0},i.rotationToNextCase[.5]={mx:0,my:1},i.rotationToNextCase[1]={mx:-1,my:0},i.rotationToNextCase[1.5]={mx:0,my:-1},i.rotationToNextCase[-.5]={mx:0,my:-1},i.rotationToNextCase[-1]={mx:-1,my:0},i.rotationToNextCase[-1.5]={mx:0,my:1},i}return E(e,t),e.prototype.setUp=function(t,e){this.car=t,this.lidar=e,this.state=[];for(var i=0;i=this.mapSizeY||t<0||t>=this.mapSizeX||0==this.map[e][t])return!1;if(i&&this.level.getRoad(e,t).cars.length>0)return!1;return!0},e.prototype.autoRotation=function(){var t=d+d/3,e=d-d/3.5,i=d;if(this.car.next_road){var o=Math.abs((this.car.next_road.outroad.y+d/2-this.car.y)*this.car.next_road.outroad_np.my),n=Math.abs((this.car.next_road.outroad.x+d/2-this.car.x)*this.car.next_road.outroad_np.mx);if(0==this.car.next_road.rotation_type&&othis.car.next_road.outroad.x+d/2?(this.car.x=this.car.next_road.outroad.x+d-d/2-d/4,this.car.y=this.car.next_road.outroad.y):0!=n&&this.car.ythis.car.next_road.outroad.y+d/2&&(this.car.y=this.car.next_road.outroad.y+d-d/2-d/4,this.car.x=this.car.next_road.outroad.x),void(this.car.next_road=void 0)}else{var a=this.car.rotation/Math.PI,r=(this.car.rotation/Math.PI+.5)%2,s=(this.car.rotation/Math.PI-.5)%2,h=this.rotationToNextCase[a],c=this.rotationToNextCase[r],l=this.rotationToNextCase[s],p=!1,u=this.car.mx+h.mx,v=this.car.my+h.my;if(this.isRoad(u,v)?this.car.haveTurned||this.car.turnedRandom?!this.car.haveTurned&&this.car.turnedRandom<.5&&this.isRoad(this.car.mx+c.mx,this.car.my+c.my)?(p=!0,this.car.optionalTurn=!0):!this.car.haveTurned&&this.car.turnedRandom>=.5&&this.isRoad(this.car.mx+l.mx,this.car.my+l.my)&&(p=!0,this.car.optionalTurn=!0):this.car.turnedRandom=Math.random():(p=!0,this.car.optionalTurn=!1),p)for(var y=[(a+.5)%2,(a-.5)%2,null],m=0;m=1&&n&&n.core.line==i&&(i=0==i?1:0),e.line=i,e.x=t.x,e.y=t.y,e.x+=d/2,e.y+=d/2;var a=1*d/4;t.orientation=Math.floor(t.orientation/e.rotationStep)*e.rotationStep;var r=-t.orientation*Math.PI,s=0*Math.cos(r)-a*Math.sin(r),h=Math.cos(r)*a+0*Math.sin(r),c=0==i?1:-1,l=0==i?0:Math.PI;e.x+=c*Math.round(s),e.y+=c*Math.round(h),e.mx=Math.floor(e.x/d),e.my=Math.floor(e.y/d),e.rotation=r+l,e.checkAndsetNewRoad(t)},t.prototype.createAsset=function(t,e,i,o){var n=new a(i[t]);n.obstacle=!1,n.type=o,n.mapId=0,n.x=e.x,n.y=e.y,this.level.addChild(n),this.assets.push(n)},t.prototype.createMap=function(t,e,i,o){if(void 0===o&&(o=!0),o)for(var n=0;n0?o=-1:e||(o=-1),o},e.prototype.getLastReward=function(){return this.lastReward},e.prototype.step=function(t,e){void 0===e&&(e=null);for(var i=0;i0&&0==this.car.a?this.car.v=Math.max(0,this.car.v-.01):this.car.v<0&&0==this.car.a&&(this.car.v=Math.min(0,this.car.v+.01)),this.car.a>0&&this.car.v>=0?this.car.v=Math.min(1.5,this.car.v+.01*this.car.a):this.car.a>0&&this.car.v<0&&(this.car.v=Math.min(1.5,this.car.v+.03*this.car.a)),this.car.a<0&&this.car.v<=0?this.car.v=Math.max(-1.5,this.car.v+.01*this.car.a):this.car.a<0&&this.car.v>0&&(this.car.v=Math.max(-1.5,this.car.v+.03*this.car.a)),0==this.car.yaw_rate)this.car.x+=this.car.v*Math.cos(this.car.rotation)*t,this.car.y+=this.car.v*Math.sin(this.car.rotation)*t;else{var e=.01*this.car.yaw_rate*(this.car.v+.001)*Math.PI;this.car.x+=this.car.v/e*(Math.sin(this.car.rotation+e*t)-Math.sin(this.car.rotation)),this.car.y+=this.car.v/e*(Math.cos(this.car.rotation)-Math.cos(this.car.rotation+e*t)),this.car.rotation+=e*t}this.car.mx=Math.floor(this.car.x/d),this.car.my=Math.floor(this.car.y/d),this.car.checkAndsetNewRoad(),this.lidar.x=this.car.x,this.lidar.y=this.car.y,this.lidar.rotation=this.car.rotation;var i=this.detectInteractions(!0,!0),o=i.agentCollisions,n=i.onRoad;return o.length>0&&(this.car.v=0,this.car.vy=0),{agentCollisions:o,onRoad:n}},e}(M),j={env:k,editor:U,level:{fullCity:"embedded://level/fullCity",level1:"embedded://level/level1",level2:"embedded://level/level2",level0:"embedded://level/level0"},motion:{BasicMotion:R,ControlMotion:B}};e.default=j}]).default; \ No newline at end of file +window.metacar=function(t){var e={};function i(o){if(e[o])return e[o].exports;var n=e[o]={i:o,l:!1,exports:{}};return 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0,this.core.carId=C,C+=1,this.core.agent=!1,this.core.obstacle=!0,this.core.mapId=y.CAR,o.lidar&&(this.createLidar(o.lidarInfo),this.core.lidar=this.lidar),o.motionEngine?(this.motion=o.motionEngine,this.motion.setUp(this.core,this.lidar),this.core.rotationStep=.5):this.core.rotationStep=.5,this.core.mx=this.info.mx,this.core.my=this.info.my,this.core.x=this.info.mx*d,this.core.y=this.info.my*d,this.core.checkAndsetNewRoad=function(t){return n.checkAndsetNewRoad(t)};var r=this.level.getRoad(this.core.my,this.core.mx);this.core.line=0,r&&r.setCarPosition(this.core,this.info.line)}return t.prototype.checkAndsetNewRoad=function(t){var e=this.core.road;t||(t=this.level.getRoad(this.core.my,this.core.mx)),t!=e&&(e&&e.cars.splice(e.cars.indexOf(this.core.carId),1),this.core.road=t,this.core.haveTurned=!1,this.core.optionalTurn=!1,this.core.turnedRandom=void 0,this.core.next_road=!1,t&&t.cars.push(this.core.carId))},t.prototype.reset=function(){this.core.mx=this.info.mx,this.core.my=this.info.my;var t=this.level.getRoad(this.core.my,this.core.mx);t&&t.setCarPosition(this.core,this.info.line,!0)},t.prototype.getState=function(){var t={},e=[];return this.motion.state.map(function(t){e=e.concat(t)}),e.push(this.core.v),t.linear=e,t.lidar=this.motion.state.map(function(t){return t.slice()}),t.v=this.core.v,t.a=this.core.last_a,t.steering=this.core.last_yaw_rate,t},t.prototype.step=function(t,e){if(void 0===e&&(e=null),null==e)var i=this.motion.step(t),o=i.agentCollisions,n=i.onRoad;else{var a=this.motion.actionStep(t,e);o=a.agentCollisions,n=a.onRoad}return{agentCollisions:o,onRoad:n}},t.prototype.createLidar=function(t){t.pts=t.pts||5,t.width=t.width||4,t.height=t.height||5,t.pos=t.pos||0,this.lidar=new s,this.lidar.lidarPts=[],this.lidar.collisionPts=[];var e=new r;e.pt=!1,e.alpha=.3,e.beginFill(5329233),e.drawRect(0,0,this.core.width*t.width,this.core.height*t.height),e.endFill();var i=new r;i.pt=!1,i.alpha=0,i.beginFill(5308416),i.drawRect(this.core.width-5,e.height/2,3,3),i.endFill();var o=new r;o.pt=!1,o.alpha=0,o.beginFill(5308416),o.drawRect(-1,e.height/2-2,3,3),o.endFill();var n=new r;n.pt=!1,n.alpha=0,n.beginFill(5308416),n.drawRect(this.core.width/2,e.height/2-this.core.height/2+5,3,3),n.endFill();var a=new r;a.pt=!1,a.alpha=0,a.beginFill(5308416),a.drawRect(this.core.width/2,e.height/2+this.core.height/2-8,3,3),a.endFill(),o.x-=t.pos*this.core.width,i.x-=t.pos*this.core.width,a.x-=t.pos*this.core.width,n.x-=t.pos*this.core.width;for(var h=e.width/t.pts,c=e.height/t.pts,l=t.pts-1;l>=0;l--)for(var d=0;do.x&&i.xo.y&&i.y=4||!t)&&(r=!0),{agentCollisions:o,onRoad:r}},t.prototype.setState=function(t){for(var e=0,i=0;ithis.state[o][n]||t[a].mapId==y.ROAD&&this.state[o][n]==y.DEFAULT)&&(this.state[o][n]=t[a].mapId)}e+=1}},t}(),b=(I=Object.setPrototypeOf||{__proto__:[]}instanceof Array&&function(t,e){t.__proto__=e}||function(t,e){for(var i in e)e.hasOwnProperty(i)&&(t[i]=e[i])},function(t,e){function i(){this.constructor=t}I(t,e),t.prototype=null===e?Object.create(e):(i.prototype=e.prototype,new i)}),R=function(t){function e(e,i){var o=t.call(this,e)||this;return o.maxSpeed=1,o.rotationStep=i.rotationStep||.5,o.actions=i.actions||["UP","LEFT","RIGHT","DOWN","WAIT"],o}return b(e,t),e.prototype.setUp=function(t,e){this.car=t,this.lidar=e,this.state=[];for(var i=0;i0&&(this.car.v=0,this.car.vy=0),{agentCollisions:i,onRoad:o}},e}(M),E=function(){var t=Object.setPrototypeOf||{__proto__:[]}instanceof Array&&function(t,e){t.__proto__=e}||function(t,e){for(var i in e)e.hasOwnProperty(i)&&(t[i]=e[i])};return function(e,i){function o(){this.constructor=e}t(e,i),e.prototype=null===i?Object.create(i):(o.prototype=i.prototype,new o)}}(),S=function(t){function e(e){var i=t.call(this,e)||this;return i.map=i.level.getMap(),i.mapSizeY=i.map.length,i.mapSizeX=i.map[0].length,i.rotationStep=.5,i.rotationToNextCase={},i.rotationToNextCase[0]={mx:1,my:0},i.rotationToNextCase[.5]={mx:0,my:1},i.rotationToNextCase[1]={mx:-1,my:0},i.rotationToNextCase[1.5]={mx:0,my:-1},i.rotationToNextCase[-.5]={mx:0,my:-1},i.rotationToNextCase[-1]={mx:-1,my:0},i.rotationToNextCase[-1.5]={mx:0,my:1},i}return E(e,t),e.prototype.setUp=function(t,e){this.car=t,this.lidar=e,this.state=[];for(var i=0;i=this.mapSizeY||t<0||t>=this.mapSizeX||0==this.map[e][t])return!1;if(i&&this.level.getRoad(e,t).cars.length>0)return!1;return!0},e.prototype.autoRotation=function(){var t=d+d/3,e=d-d/3.5,i=d;if(this.car.next_road){var o=Math.abs((this.car.next_road.outroad.y+d/2-this.car.y)*this.car.next_road.outroad_np.my),n=Math.abs((this.car.next_road.outroad.x+d/2-this.car.x)*this.car.next_road.outroad_np.mx);if(0==this.car.next_road.rotation_type&&othis.car.next_road.outroad.x+d/2?(this.car.x=this.car.next_road.outroad.x+d-d/2-d/4,this.car.y=this.car.next_road.outroad.y):0!=n&&this.car.ythis.car.next_road.outroad.y+d/2&&(this.car.y=this.car.next_road.outroad.y+d-d/2-d/4,this.car.x=this.car.next_road.outroad.x),void(this.car.next_road=void 0)}else{var a=this.car.rotation/Math.PI,r=(this.car.rotation/Math.PI+.5)%2,s=(this.car.rotation/Math.PI-.5)%2,h=this.rotationToNextCase[a],c=this.rotationToNextCase[r],l=this.rotationToNextCase[s],p=!1,y=this.car.mx+h.mx,u=this.car.my+h.my;if(this.isRoad(y,u)?this.car.haveTurned||this.car.turnedRandom?!this.car.haveTurned&&this.car.turnedRandom<.5&&this.isRoad(this.car.mx+c.mx,this.car.my+c.my)?(p=!0,this.car.optionalTurn=!0):!this.car.haveTurned&&this.car.turnedRandom>=.5&&this.isRoad(this.car.mx+l.mx,this.car.my+l.my)&&(p=!0,this.car.optionalTurn=!0):this.car.turnedRandom=Math.random():(p=!0,this.car.optionalTurn=!1),p)for(var x=[(a+.5)%2,(a-.5)%2,null],v=0;v=1&&n&&n.core.line==i&&(i=0==i?1:0),e.line=i,e.a=0,e.v=0,e.x=t.x,e.y=t.y,e.x+=d/2,e.y+=d/2;var a=1*d/4;t.orientation=Math.floor(t.orientation/e.rotationStep)*e.rotationStep;var r=-t.orientation*Math.PI,s=0*Math.cos(r)-a*Math.sin(r),h=Math.cos(r)*a+0*Math.sin(r),c=0==i?1:-1,l=0==i?0:Math.PI;e.x+=c*Math.round(s),e.y+=c*Math.round(h),e.mx=Math.floor(e.x/d),e.my=Math.floor(e.y/d),e.rotation=r+l,e.checkAndsetNewRoad(t)},t.prototype.createAsset=function(t,e,i,o){var n=new a(i[t]);n.obstacle=!1,n.type=o,n.mapId=0,n.x=e.x,n.y=e.y,this.level.addChild(n),this.assets.push(n)},t.prototype.createMap=function(t,e,i,o){if(void 0===o&&(o=!0),o)for(var n=0;n0?o=-1:e||(o=-1),o},e.prototype.getLastReward=function(){return this.lastReward},e.prototype.step=function(t,e,i){if(void 0===e&&(e=null),void 0===i&&(i=!0),!i||this.steping){for(var o=0;o0&&0==this.car.a?this.car.v=Math.max(0,this.car.v-.01):this.car.v<0&&0==this.car.a&&(this.car.v=Math.min(0,this.car.v+.01)),this.car.a>0&&this.car.v>=0?this.car.v=Math.min(this.maxSpeed,this.car.v+.005*this.car.a):this.car.a>0&&this.car.v<0&&(this.car.v=Math.min(this.maxSpeed,this.car.v+.03*this.car.a)),this.car.a<0&&this.car.v<=0?this.car.v=Math.max(-this.maxSpeed,this.car.v+.005*this.car.a):this.car.a<0&&this.car.v>0&&(this.car.v=Math.max(-this.maxSpeed,this.car.v+.03*this.car.a)),0==this.car.yaw_rate)this.car.x+=this.car.v*Math.cos(this.car.rotation)*t,this.car.y+=this.car.v*Math.sin(this.car.rotation)*t;else{var e=.01*this.car.yaw_rate*(this.car.v+.001)*Math.PI;this.car.x+=this.car.v/e*(Math.sin(this.car.rotation+e*t)-Math.sin(this.car.rotation)),this.car.y+=this.car.v/e*(Math.cos(this.car.rotation)-Math.cos(this.car.rotation+e*t)),this.car.rotation+=e*t}this.car.mx=Math.floor(this.car.x/d),this.car.my=Math.floor(this.car.y/d),this.car.checkAndsetNewRoad(),this.lidar.x=this.car.x,this.lidar.y=this.car.y,this.lidar.rotation=this.car.rotation;var i=this.detectInteractions(!0,!0),o=i.agentCollisions,n=i.onRoad;return o.length>0&&(this.car.v=0,this.car.vy=0),{agentCollisions:o,onRoad:n}},e}(M),j={env:k,editor:U,level:{fullCity:"embedded://level/fullCity",level1:"embedded://level/level1",level2:"embedded://level/level2",level3:"embedded://level/level3",level0:"embedded://level/level0"},motion:{BasicMotion:R,ControlMotion:B}};e.default=j}]).default; \ No newline at end of file diff --git a/demo/webapp/ddpg-traffic.html b/demo/webapp/ddpg-traffic.html new file mode 100644 index 0000000..f522764 --- /dev/null +++ b/demo/webapp/ddpg-traffic.html @@ -0,0 +1,66 @@ + + + + + Metacar: Continous control in traffic + + + + + + +
+
+

Metacar

+ +
+
+ +
+ +
+
+ +
+

+

Current state (Lidar points)




+
+

+ The algorithm is based on the following papers: +

    +
  • Deep Deterministic Policy Gradients (DDPG): paper
    • +
  • Parameter Space Noise for Exploration: paper
    • +
+
+ You can use the arrow keys to control the car by yourself.

+ + The motion control is based on two continuous values for the throttle and steering angle of the car. + +

The left window gives you an overview of what the autonomous vehicle (in red) sees.

+ + You can find the code of this demo here and here. +

+
+ +
+
+ + + + + + + + + + + + + + + + + + diff --git a/demo/webapp/ddpg.html b/demo/webapp/ddpg.html new file mode 100644 index 0000000..da58b53 --- /dev/null +++ b/demo/webapp/ddpg.html @@ -0,0 +1,66 @@ + + + + + Metacar: Continous control + + + + + + +
+
+

Metacar

+ +
+
+ +
+ +
+
+ +
+

+

Current state (Lidar points)




+
+

+ The algorithm is based on the following papers: +

    +
  • Deep Deterministic Policy Gradients (DDPG): paper
    • +
  • Parameter Space Noise for Exploration: paper
    • +
+
+ You can use the arrow keys to control the car by yourself.

+ + The motion control is based on two continuous values for the throttle and steering angle of the car. + +

The left window gives you an overview of what the autonomous vehicle (in red) sees.

+ + You can find the code of this demo here. +

+
+ +
+
+ + + + + + + + + + + + + + + + + + diff --git a/demo/webapp/index.html b/demo/webapp/index.html index cec0729..8a3f655 100644 --- a/demo/webapp/index.html +++ b/demo/webapp/index.html @@ -37,45 +37,70 @@


- +

-
Q-learning with table +
Discrete Actions

+ Algorithm: Q-learning with table

In this example, the goal is to demonstrate a simple working example of a reinforcement learning algorithm using the metacar environment. You can check out - the demo or take a look at the medium tutorial. + the demo or take a look at the medium tutorial.

-
- +

-
Policy Monte carlo +
Discrete Actions in traffic

- Here is an axample of a policy gradient algorithm based on Monte Carlo exploration. - The neural network is build using tensorflow.js. Check out the demo. + Algorithm: Policy gradient algorithm based on Monte Carlo exploration (REINFORCE).

+ + The neural network is build using tensorflow.js. Check out the demo.

-
- +
+

-
Full control +
Continous Actions

+
+
    +
  • Deep Deterministic Policy Gradients (DDPG): paper
    • +
  • Parameter Space Noise for Exploration: paper
    • +
+

- This level is not solved yet (working on it). However, you can contribute to the project by solving it and sharing your implementation!

-

+ The control is based on two continuous values for the throttle and steering angle of the car. + The model is made using tensorflow.js

+
+ +

+
Continous Actions in traffic +

+
+
    +
  • Deep Deterministic Policy Gradients (DDPG): paper
    • +
  • Parameter Space Noise for Exploration: paper
    • +
+

+ The control is based on two continuous values for the throttle and steering angle of the car. + The model is made using tensorflow.js +

+

+
+ +
- +


Create your own level!

diff --git a/demo/webapp/level2.html b/demo/webapp/level2.html deleted file mode 100644 index 78bf318..0000000 --- a/demo/webapp/level2.html +++ /dev/null @@ -1,47 +0,0 @@ - - - - - Metacar: Full control - - - - - - -
-
-

Metacar

- -
-
- -
- -
-
- -
-

Current state (Lidar points)



-
-

- You can use the arrow keys to control the car by yourself.

- - This level is not solved yet (working on it). However, You can contribute to the project by solving it and sharing your implementation!

- - The motion control is based on two continuous values for the throttle and steering angle of the car. - -

The left window gives you an overview of what the autonomous vehicle (in red) sees.

-

-
- - - - - - - - - diff --git a/demo/webapp/level1.html b/demo/webapp/policy.html similarity index 88% rename from demo/webapp/level1.html rename to demo/webapp/policy.html index 9938b98..6f4a585 100644 --- a/demo/webapp/level1.html +++ b/demo/webapp/policy.html @@ -2,7 +2,7 @@ - Metacar: Policy gradient demo + Metacar: Discrete Actions in traffic. @@ -25,7 +25,7 @@

-

Current state (Lidar points)



+

Current state (Lidar points)




Policy Gradient algorithm based on Monte Carlo exploration.
@@ -45,9 +45,10 @@ - - + + + diff --git a/demo/webapp/public/img/ddpg-traffic.png b/demo/webapp/public/img/ddpg-traffic.png new file mode 100644 index 0000000..0945fbe Binary files /dev/null and b/demo/webapp/public/img/ddpg-traffic.png differ diff --git a/demo/webapp/public/img/ddpg.png b/demo/webapp/public/img/ddpg.png new file mode 100644 index 0000000..130f3c9 Binary files /dev/null and b/demo/webapp/public/img/ddpg.png differ diff --git a/demo/webapp/public/img/level0.png b/demo/webapp/public/img/level0.png index 45fc4db..5238685 100644 Binary files a/demo/webapp/public/img/level0.png and b/demo/webapp/public/img/level0.png differ diff --git a/demo/webapp/public/js/ddpg-traffic/index.js b/demo/webapp/public/js/ddpg-traffic/index.js new file mode 100644 index 0000000..db93f4d --- /dev/null +++ b/demo/webapp/public/js/ddpg-traffic/index.js @@ -0,0 +1,73 @@ + +let levelUrl = metacar.level.level3; + +var env = new metacar.env("canvas", levelUrl); + +env.setAgentMotion(metacar.motion.ControlMotion, {}); +env.setAgentLidar({pts: 7, width: 3, height: 7, pos: -0.5}) + +// js/DDPG/ddpg.js +var agent = new DDPGAgent(env, { + stateSize: 50, + resetEpisode: true, + desiredActionStddev: 0.4, + initialStddev: 0.4, + actorFirstLayerSize: 128, + actorSecondLayerSize: 64, + criticFirstLayerSSize: 128, + criticFirstLayerASize: 128, + criticSecondLayerSize: 64, + nbEpochs: 1000 +}); + +initMetricsContainer("statContainer", ["Reward", "ActorLoss", "CriticLoss", "EpisodeDuration", "NoiseDistance"]); + +let it = 0; +env.loop(() => { + let state = env.getState(); + displayState("realtime_viewer", state.lidar, 200, 200); + let reward = env.getLastReward(); + const qValue = agent.getQvalue(state.linear, [state.a, state.steering]); + if (it % 10 == 0) + displayScores("realtime_viewer", [qValue, state.a, state.steering], reward, ["Q(a, s)", "Acceleration", "Steering Angle"]); + it += 1; +}); + +env.load().then(() => { + + env.addEvent("train [Background]", () => { + let train = confirm("The training process takes some time and might slow this tab. Do you want to continue? \n You can also load a pre-trained model."); + if (train){ + env.render(false); + agent.train(false); + } + }); + + env.addEvent("Train [Show the training]", () => { + env.steping(false); + agent.train(true); + }); + + env.addEvent("play", () => { + agent.play(); + }); + + env.addEvent("stop", () => { + agent.stop(); + }); + + env.addEvent("reset_env"); + + env.addEvent("save", () => { + agent.save("model-ddpg-traffic"); + }); + + env.addEvent("load", () => { + agent.restore("ddpg-traffic", "model-ddpg-traffic-epoch-120"); + }); +}); + + + + + diff --git a/demo/webapp/public/js/ddpg/ddpg.js b/demo/webapp/public/js/ddpg/ddpg.js new file mode 100644 index 0000000..1f79b99 --- /dev/null +++ b/demo/webapp/public/js/ddpg/ddpg.js @@ -0,0 +1,271 @@ + +function logTfMemory(){ + let mem = tf.memory(); + console.log("numBytes:" + mem.numBytes + + "\nnumBytesInGPU:" + mem.numBytesInGPU + + "\nnumDataBuffers:" + mem.numDataBuffers + + "\nnumTensors:" + mem.numTensors); +} + +// This class is called from js/DDPG/ddpg_agent.js +class DDPG { + + /** + * @param config (Object) + * @param actor (Actor class) + * @param critic (Critic class) + * @param memory (Memory class) + * @param noise (Noise class) + */ + constructor(actor, critic, memory, noise, config){ + this.actor = actor; + this.critic = critic; + this.memory = memory; + this.noise = noise; + this.config = config; + this.tfGamma = tf.scalar(config.gamma); + + // Inputs + this.obsInput = tf.input({batchShape: [null, this.config.stateSize]}); + this.actionInput = tf.input({batchShape: [null, this.config.nbActions]}); + + // Randomly Initialize actor network μ(s) + this.actor.buildModel(this.obsInput); + // Randomly Initialize critic network Q(s, a) + this.critic.buildModel(this.obsInput, this.actionInput); + + + // Define in js/DDPG/models.js + // Init target network Q' and μ' with the same weights + this.actorTarget = copyModel(this.actor, Actor); + this.criticTarget = copyModel(this.critic, Critic); + // Perturbed Actor (See parameter space noise Exploration paper) + this.perturbedActor = copyModel(this.actor, Actor); + //this.adaptivePerturbedActor = copyModel(this.actor, Actor); + + this.setLearningOp(); + } + + setLearningOp(){ + this.criticWithActor = (tfState) => { + return tf.tidy(() => { + const tfAct = this.actor.predict(tfState); + return this.critic.predict(tfState, tfAct); + }); + }; + this.criticTargetWithActorTarget = (tfState) => { + return tf.tidy(() => { + const tfAct = this.actorTarget.predict(tfState); + return this.criticTarget.predict(tfState, tfAct); + }); + }; + + this.actorOptimiser = tf.train.adam(this.config.actorLr); + this.criticOptimiser = tf.train.adam(this.config.criticLr); + + this.criticWeights = []; + for (let w = 0; w < this.critic.model.trainableWeights.length; w++){ + this.criticWeights.push(this.critic.model.trainableWeights[w].val); + } + this.actorWeights = []; + for (let w = 0; w < this.actor.model.trainableWeights.length; w++){ + this.actorWeights.push(this.actor.model.trainableWeights[w].val); + } + + assignAndStd(this.actor, this.perturbedActor, this.noise.currentStddev, this.config.seed); + } + + + /** + * Distance Measure for DDPG + * See parameter space noise Exploration paper + * @param observations (Tensor2d) Observations + */ + distanceMeasure(observations) { + return tf.tidy(() => { + const pertubedPredictions = this.perturbedActor.model.predict(observations); + const predictions = this.actor.model.predict(observations); + + const distance = tf.square(pertubedPredictions.sub(predictions)).mean().sqrt(); + return distance; + }); + } + + /** + * AdaptParamNoise + */ + adaptParamNoise(){ + const batch = this.memory.getBatch(this.config.batchSize); + if (batch.obs0.length == 0){ + assignAndStd(this.actor, this.perturbedActor, this.noise.currentStddev, this.config.seed); + return [0]; + } + + let distanceV = null; + + if (batch.obs0.length > 0){ + const tfObs0 = tf.tensor2d(batch.obs0); + const distance = this.distanceMeasure(tfObs0); + + assignAndStd(this.actor, this.perturbedActor, this.noise.currentStddev, this.config.seed); + + distanceV = distance.buffer().values; + this.noise.adapt(distanceV[0]); + + distance.dispose(); + tfObs0.dispose(); + } + + return distanceV; + } + + /** + * Get the estimation of the Q value given the state + * and the action + * @param state number[] + * @param action [a, steering] + */ + getQvalue(state, a){ + const st = tf.tensor2d([state]); + const tfa = tf.tensor2d([a]); + const q = this.critic.model.predict([st, tfa]); + const v = q.buffer().values + st.dispose(); + tfa.dispose(); + q.dispose(); + return v[0]; + } + + /** + * @param observation (tf.tensor2d) + * @return (tf.tensor1d) + */ + predict(observation){ + const tfActions = this.actor.model.predict(observation); + return tfActions; + } + + /** + * @param observation (tf.tensor2d) + * @return (tf.tensor1d) + */ + perturbedPrediction(observation){ + const tfActions = this.perturbedActor.model.predict(observation); + return tfActions; + } + + /** + * Update the two target network + */ + targetUpdate(){ + // Define in js/DDPG/models.js + targetUpdate(this.criticTarget, this.critic, this.config); + targetUpdate(this.actorTarget, this.actor, this.config); + } + + trainCritic(batch, tfActions, tfObs0, tfObs1, tfRewards, tfTerminals){ + + let costs; + + const criticLoss = this.criticOptimiser.minimize(() => { + const tfQPredictions0 = this.critic.model.predict([tfObs0, tfActions]); + const tfQPredictions1 = this.criticTargetWithActorTarget(tfObs1); + + const tfQTargets = tfRewards.add(tf.scalar(1).sub(tfTerminals).mul(this.tfGamma).mul(tfQPredictions1)); + + const erros = tf.sub(tfQTargets, tfQPredictions0).square(); + costs = erros.buffer().values; + + return erros.mean(); + }, true, this.criticWeights); + + // For experience Replay + this.memory.appendBackWithCost(batch, costs); + + const loss = criticLoss.buffer().values[0]; + criticLoss.dispose(); + + targetUpdate(this.criticTarget, this.critic, this.config); + + return loss; + } + + trainActor(tfObs0){ + + const actorLoss = this.actorOptimiser.minimize(() => { + const tfQPredictions0 = this.criticWithActor(tfObs0); + return tf.mean(tfQPredictions0).mul(tf.scalar(-1.)) + }, true, this.actorWeights); + + targetUpdate(this.actorTarget, this.actor, this.config); + + const loss = actorLoss.buffer().values[0]; + actorLoss.dispose(); + + return loss; + } + + getTfBatch(){ + // Get batch + const batch = this.memory.popBatch(this.config.batchSize); + // Convert to tensors + const tfActions = tf.tensor2d(batch.actions); + const tfObs0 = tf.tensor2d(batch.obs0); + const tfObs1 = tf.tensor2d(batch.obs1); + const _tfRewards = tf.tensor1d(batch.rewards); + const _tfTerminals = tf.tensor1d(batch.terminals); + + const tfRewards = _tfRewards.expandDims(1); + const tfTerminals = _tfTerminals.expandDims(1); + + _tfRewards.dispose(); + _tfTerminals.dispose(); + + return { + batch, tfActions, tfObs0, tfObs1, tfRewards, tfTerminals + } + } + + optimizeCritic(){ + const {batch, tfActions, tfObs0, tfObs1, tfRewards, tfTerminals} = this.getTfBatch(); + + const loss = this.trainCritic(tfActions, tfObs0, tfObs1, tfRewards, tfTerminals); + + tfActions.dispose(); + tfObs0.dispose(); + tfObs1.dispose(); + tfRewards.dispose(); + tfTerminals.dispose(); + + return loss; + } + + optimizeActor(){ + const {batch, tfActions, tfObs0, tfObs1, tfRewards, tfTerminals} = this.getTfBatch(); + + const loss = this.trainActor(tfObs0); + + tfActions.dispose(); + tfObs0.dispose(); + tfObs1.dispose(); + tfRewards.dispose(); + tfTerminals.dispose(); + + return loss; + } + + optimizeCriticActor(){ + const {batch, tfActions, tfObs0, tfObs1, tfRewards, tfTerminals} = this.getTfBatch(); + + const lossC = this.trainCritic(batch, tfActions, tfObs0, tfObs1, tfRewards, tfTerminals); + const lossA = this.trainActor(tfObs0); + + tfActions.dispose(); + tfObs0.dispose(); + tfObs1.dispose(); + tfRewards.dispose(); + tfTerminals.dispose(); + + return {lossC, lossA}; + } +} \ No newline at end of file diff --git a/demo/webapp/public/js/ddpg/ddpg_agent.js b/demo/webapp/public/js/ddpg/ddpg_agent.js new file mode 100644 index 0000000..95f922f --- /dev/null +++ b/demo/webapp/public/js/ddpg/ddpg_agent.js @@ -0,0 +1,232 @@ +// This class is called from js/DDPG/index.js +class DDPGAgent { + + /** + * @param env (metacar.env) Set in js/DDPG/index.js + */ + constructor(env, config){ + + this.stopTraining = false; + this.env = env; + + config = config || {}; + // Default Config + this.config = { + "stateSize": config.stateSize || 17, + "nbActions": config.nbActions || 2, + "seed": config.seed || 0, + "batchSize": config.batchSize || 128, + "actorLr": config.actorLr || 0.0001, + "criticLr": config .criticLr || 0.001, + "memorySize": config.memorySize || 30000, + "gamma": config.gamme || 0.99, + "noiseDecay": config.noiseDecay || 0.99, + "rewardScale": config.rewardScale || 1, + "nbEpochs": config.nbEpochs || 200, + "nbEpochsCycle": config.nbEpochsCycle || 10, + "nbTrainSteps": config.nbTrainSteps || 110, + "tau": config.tau || 0.008, + "initialStddev": config.initialStddev || 0.1, + "desiredActionStddev": config.desiredActionStddev || 0.1, + "adoptionCoefficient": config.adoptionCoefficient || 1.01, + "actorFirstLayerSize": config.actorFirstLayerSize || 64, + "actorSecondLayerSize": config.actorSecondLayerSize || 32, + "criticFirstLayerSSize": config.criticFirstLayerSSize || 64, + "criticFirstLayerASize": config.criticFirstLayerASize || 64, + "criticSecondLayerSize": config.criticSecondLayerSize || 32, + "maxStep": config.maxStep || 800, + "stopOnRewardError": config.stopOnRewardError != undefined ? config.stopOnRewardError:true, + "resetEpisode": config.resetEpisode != undefined ? config.resetEpisode:false, + "saveDuringTraining": config.saveDuringTraining || false, + "saveInterval": config.saveInterval || 20 + }; + this.epoch = 0; + // From js/DDPG/noise.js + this.noise = new AdaptiveParamNoiseSpec(this.config); + + // Configure components. + + // Buffer replay + // The baseline use 1e6 but this size should be enough for this problem + this.memory = new PrioritizedMemory(this.config.memorySize); + // Actor and Critic are from js/DDPG/models.js + this.actor = new Actor(this.config); + this.critic = new Critic(this.config); + + // Seed javascript + Math.seedrandom(0); + + this.rewardsList = []; + this.epiDuration = []; + + // DDPG + this.ddpg = new DDPG(this.actor, this.critic, this.memory, this.noise, this.config); + } + + save(name){ + /* + Save the network + */ + this.ddpg.critic.model.save('downloads://critic-' + name); + this.ddpg.actor.model.save('downloads://actor-'+ name); + } + + async restore(folder, name){ + /* + Restore the weights of the network + */ + const critic = await tf.loadModel('https://metacar-project.com/public/models/'+folder+'/critic-'+name+'.json'); + const actor = await tf.loadModel("https://metacar-project.com/public/models/"+folder+"/actor-"+name+".json"); + + this.ddpg.critic = copyFromSave(critic, Critic, this.config, this.ddpg.obsInput, this.ddpg.actionInput); + this.ddpg.actor = copyFromSave(actor, Actor, this.config, this.ddpg.obsInput, this.ddpg.actionInput); + + // Define in js/DDPG/models.js + // Init target network Q' and μ' with the same weights + this.ddpg.actorTarget = copyModel(this.ddpg.actor, Actor); + this.ddpg.criticTarget = copyModel(this.ddpg.critic, Critic); + // Perturbed Actor (See parameter space noise Exploration paper) + this.ddpg.perturbedActor = copyModel(this.ddpg.actor, Actor); + //this.adaptivePerturbedActor = copyModel(this.actor, Actor); + this.ddpg.setLearningOp(); + } + + /** + * Play one step + */ + play(){ + // Get the current state + const state = this.env.getState().linear; + // Pick an action + const tfActions = this.ddpg.predict(tf.tensor2d([state])); + const actions = tfActions.buffer().values; + agent.env.step([actions[0], actions[1]]); + tfActions.dispose(); + } + + /** + * Get the estimation of the Q value given the state + * and the action + * @param state number[] + * @param action [a, steering] + */ + getQvalue(state, a){ + return this.ddpg.getQvalue(state, a); + } + + stop(){ + this.stopTraining = true; + } + + /** + * Step into the training environement + * @param tfPreviousStep (tf.tensor2d) Current state + * @param mPreviousStep number[] + * @return {done, state} One boolean and the new state + */ + stepTrain(tfPreviousStep, mPreviousStep){ + // Get actions + const tfActions = this.ddpg.perturbedPrediction(tfPreviousStep); + // Step in the environment with theses actions + let mAcions = tfActions.buffer().values; + let mReward = this.env.step([mAcions[0], mAcions[1]]); + this.rewardsList.push(mReward); + // Get the new observations + let mState = this.env.getState().linear; + let tfState = tf.tensor2d([mState]); + let mDone = 0; + if (mReward == -1 && this.config.stopOnRewardError){ + mDone = 1; + } + // Add the new tuple to the buffer + this.ddpg.memory.append(mPreviousStep, [mAcions[0], mAcions[1]], mReward, mState, mDone); + // Dispose tensors + tfPreviousStep.dispose(); + tfActions.dispose(); + + return {mDone, mState, tfState}; + } + + /** + * Optimize models and log states + */ + _optimize(){ + this.ddpg.noise.desiredActionStddev = Math.max(0.1, this.config.noiseDecay * this.ddpg.noise.desiredActionStddev); + let lossValuesCritic = []; + let lossValuesActor = []; + console.time("Training"); + for (let t=0; t < this.config.nbTrainSteps; t++){ + let {lossC, lossA} = this.ddpg.optimizeCriticActor(); + lossValuesCritic.push(lossC); + lossValuesActor.push(lossA); + } + console.timeEnd("Training"); + console.log("desiredActionStddev:", this.ddpg.noise.desiredActionStddev); + setMetric("CriticLoss", mean(lossValuesCritic)); + setMetric("ActorLoss", mean(lossValuesActor)); + } + + /** + * Train DDPG Agent + */ + async train(realTime){ + this.stopTraining = false; + // One epoch + for (this.epoch; this.epoch < this.config.nbEpochs; this.epoch++){ + // Perform cycles. + this.rewardsList = []; + this.stepList = []; + this.distanceList = []; + document.getElementById("trainingProgress").innerHTML = "Progression: "+this.epoch+"/"+this.config.nbEpochs+"
"; + for (let c=0; c < this.config.nbEpochsCycle; c++){ + if (c%10==0){ logTfMemory(); } + let mPreviousStep = this.env.getState().linear; + let tfPreviousStep = tf.tensor2d([mPreviousStep]); + let step = 0; + + console.time("LoopTime"); + for (step=0; step < this.config.maxStep; step++){ + let rel = this.stepTrain(tfPreviousStep, mPreviousStep); + mPreviousStep = rel.mState; + tfPreviousStep = rel.tfState; + if (rel.mDone && this.config.stopOnRewardError){ + break; + } + if (this.stopTraining){ + this.env.render(true); + return; + } + if (realTime && step % 10 == 0) + await tf.nextFrame(); + } + this.stepList.push(step); + console.timeEnd("LoopTime"); + let distance = this.ddpg.adaptParamNoise(); + this.distanceList.push(distance[0]); + + if (this.config.resetEpisode){ + this.env.reset(); + } + this.env.shuffle({cars: false}); + tfPreviousStep.dispose(); + console.log("e="+ this.epoch +", c="+c); + + await tf.nextFrame(); + } + if (this.epoch > 5){ + this._optimize(); + } + if (this.config.saveDuringTraining && this.epoch % this.config.saveInterval == 0 && this.epoch != 0){ + this.save("model-ddpg-traffic-epoch-"+this.epoch); + } + setMetric("Reward", mean(this.rewardsList)); + setMetric("EpisodeDuration", mean(this.stepList)); + setMetric("NoiseDistance", mean(this.distanceList)); + await tf.nextFrame(); + } + + + this.env.render(true); + } + +}; \ No newline at end of file diff --git a/demo/webapp/public/js/ddpg/index.js b/demo/webapp/public/js/ddpg/index.js new file mode 100644 index 0000000..bbe6fd3 --- /dev/null +++ b/demo/webapp/public/js/ddpg/index.js @@ -0,0 +1,63 @@ +let levelUrl = metacar.level.level2; + +var env = new metacar.env("canvas", levelUrl); + +env.setAgentMotion(metacar.motion.ControlMotion, {}); +env.setAgentLidar({pts: 5, width: 3, height: 7, pos: -0.5}) + +// js/DDPG/ddpg.js +var agent = new DDPGAgent(env, { + stateSize: 26 +}); + +initMetricsContainer("statContainer", ["Reward", "ActorLoss", "CriticLoss", "EpisodeDuration", "NoiseDistance"]); + +let it = 0; +env.loop(() => { + let state = env.getState(); + displayState("realtime_viewer", state.lidar, 200, 200); + let reward = env.getLastReward(); + const qValue = agent.getQvalue(state.linear, [state.a, state.steering]); + if (it % 10 == 0) + displayScores("realtime_viewer", [qValue, state.a, state.steering], reward, ["Q(a, s)", "Acceleration", "Steering Angle"]); + it += 1; +}); + +env.load().then(() => { + + env.addEvent("train [Background]", () => { + let train = confirm("The training process takes some time and might slow this tab. Do you want to continue? \n You can also load a pre-trained model."); + if (train){ + env.render(false); + agent.train(false); + } + }); + + env.addEvent("Train [Show the training]", () => { + env.steping(false); + agent.train(true); + }); + + env.addEvent("shuffle", () => { + env.shuffle({cars: false}); + }) + + env.addEvent("play", () => { + agent.play(); + }); + + env.addEvent("stop", () => { + agent.stop(); + }); + + env.addEvent("reset_env"); + + env.addEvent("save", () => { + agent.save("model-ddpg-road"); + }); + + env.addEvent("load", () => { + agent.restore("ddpg-road", "model-ddpg-road") + }); +}); + diff --git a/demo/webapp/public/js/ddpg/memory.js b/demo/webapp/public/js/ddpg/memory.js new file mode 100644 index 0000000..fc8db0a --- /dev/null +++ b/demo/webapp/public/js/ddpg/memory.js @@ -0,0 +1,83 @@ + +class Memory { + + /** + * @param maxlen (number) Buffer limit + */ + constructor(maxlen){ + this.maxlen = maxlen; + this.length = 0; + this.start = 0; + + this.obs0List = Array.apply(null, Array(maxlen)).map(Number.prototype.valueOf, 0); + this.obs1List = Array.apply(null, Array(maxlen)).map(Number.prototype.valueOf, 0); + this.rewardsList = Array.apply(null, Array(maxlen)).map(Number.prototype.valueOf, 0); + this.actionsList = Array.apply(null, Array(maxlen)).map(Number.prototype.valueOf, 0); + this.terminals1List = Array.apply(null, Array(maxlen)).map(Number.prototype.valueOf, 0); + } + + /** + * @param idx (number) + */ + getItem(idx){ + if (idx < 0 || idx >= this.length){ + console.error("Memory.getItem: idx not in range."); + } + return this.data[(this.start + idx) % this.maxlen] + } + + /** + * Sample a batch + * @param batchSize (number) + * @return batch [] + */ + getBatch(batchSize){ + const arrLength = this.length; + const batch = { + 'obs0': [], + 'obs1': [], + 'rewards': [], + 'actions': [], + 'terminals': [], + }; + if (batchSize > this.length){ + return batch; + } + for (let b=0; b < batchSize; b++){ + let id = Math.floor(Math.random() * arrLength); + batch.obs0.push(this.obs0List[id]); + batch.obs1.push(this.obs1List[id]); + batch.rewards.push(this.rewardsList[id]); + batch.actions.push(this.actionsList[id]); + batch.terminals.push(this.terminals1List[id]); + } + return batch + } + + /** + * @param obs0 [] + * @param action (number) + * @param reward (number) + * @param obs1 [] + * @param terminal1 (boolean) + */ + append(obs0, action, reward, obs1, terminal1){ + if (this.length < this.maxlen){ + this.length += 1; + } + else if (this.length == this.maxlen) { + //this.obs0List[(this.start + this.length - 1) % this.maxlen].dispose(); + //this.obs1List[(this.start + this.length - 1) % this.maxlen].dispose(); + //this.actionsList[(this.start + this.length - 1) % this.maxlen].dispose(); + this.start = (this.start + 1) % this.maxlen; + } + else { + console.error("Memory.append: This should never be printed"); + } + this.obs0List[(this.start + this.length - 1) % this.maxlen] = obs0; + this.obs1List[(this.start + this.length - 1) % this.maxlen] = obs1; + this.rewardsList[(this.start + this.length - 1) % this.maxlen] = reward; + this.actionsList[(this.start + this.length - 1) % this.maxlen] = action; + this.terminals1List[(this.start + this.length - 1) % this.maxlen] = terminal1; + } +} \ No newline at end of file diff --git a/demo/webapp/public/js/ddpg/models.js b/demo/webapp/public/js/ddpg/models.js new file mode 100644 index 0000000..6ef1c3d --- /dev/null +++ b/demo/webapp/public/js/ddpg/models.js @@ -0,0 +1,245 @@ +/** + * Copy a model + * @param model Actor|Critic instance + * @param instance Actor|Critic + * @return Copy of the model + */ +function copyFromSave(model, instance, config, obs, action){ + return tf.tidy(() => { + nModel = new instance(config); + // action might be not required + nModel.buildModel(obs, action); + const weights = model.weights; + for (let m=0; m < weights.length; m++){ + nModel.model.weights[m].val.assign(weights[m].val); + } + return nModel; + }) +} + + +/** + * Copy a model + * @param model Actor|Critic instance + * @param instance Actor|Critic + * @return Copy of the model + */ +function copyModel(model, instance){ + return tf.tidy(() => { + nModel = new instance(model.config); + // action might be not required + nModel.buildModel(model.obs, model.action); + const weights = model.model.weights; + for (let m=0; m < weights.length; m++){ + nModel.model.weights[m].val.assign(weights[m].val); + } + return nModel; + }) +} + +/** + * Copy the value of of the model into the perturbedModel and + * add a random pertubation + * @param model Actor|Critic instance + * @param perturbedActor Actor|Critic instance + * @param stddev (number) + * @return Copy of the model + */ +function assignAndStd(model, perturbedModel, stddev, seed){ + return tf.tidy(() => { + const weights = model.model.trainableWeights; + for (let m=0; m < weights.length; m++){ + let shape = perturbedModel.model.trainableWeights[m].val.shape; + let randomTensor = tf.randomNormal(shape, 0, stddev, "float32", seed); + let nValue = weights[m].val.add(randomTensor); + perturbedModel.model.trainableWeights[m].val.assign(nValue); + } + }); +} + +/** + * Update the target models + * @param target Actor|Critic instance + * @param perturbedActor Actor|Critic instance + * @param config (Object) + * @return Copy of the model + */ +function targetUpdate(target, original, config){ + return tf.tidy(() => { + const originalW = original.model.trainableWeights; + const targetW = target.model.trainableWeights; + + const one = tf.scalar(1); + const tau = tf.scalar(config.tau); + + for (let m=0; m < originalW.length; m++){ + const lastValue = target.model.trainableWeights[m].val.clone(); + let nValue = tau.mul(originalW[m].val).add(targetW[m].val.mul(one.sub(tau))); + target.model.trainableWeights[m].val.assign(nValue); + const diff = lastValue.sub(target.model.trainableWeights[m].val).mean().buffer().values; + if (diff[0] == 0){ + console.warn("targetUpdate: Nothing have been changed!") + } + } + }); +} + + +class Actor{ + + /** + @param config (Object) + */ + constructor(config) { + this.stateSize = config.stateSize; + this.nbActions = config.nbActions; + this.layerNorm = config.layerNorm; + + this.firstLayerSize = config.actorFirstLayerSize; + this.secondLayerSize = config.actorSecondLayerSize; + + this.seed = config.seed; + this.config = config; + this.obs = null; + } + + /** + * + * @param obs tf.input + */ + buildModel(obs){ + this.obs = obs; + + // First layer + this.firstLayer = tf.layers.dense({ + units: this.firstLayerSize, + kernelInitializer: tf.initializers.glorotUniform({seed: this.seed}), + activation: 'relu', + useBias: true, + biasInitializer: "zeros" + }); + // Second layer + this.secondLayer = tf.layers.dense({ + units: this.secondLayerSize, + kernelInitializer: tf.initializers.glorotUniform({seed: this.seed}), + activation: 'relu', + useBias: true, + biasInitializer: "zeros" + }); + // Ouput layer + this.outputLayer = tf.layers.dense({ + units: this.nbActions, + kernelInitializer: tf.initializers.randomUniform({ + minval: 0.003, maxval: 0.003, seed: this.seed}), + activation: 'tanh', + useBias: true, + biasInitializer: "zeros" + }); + // Actor prediction + this.predict = (tfState) => { + return tf.tidy(() => { + if (tfState){ + obs = tfState; + } + + let l1 = this.firstLayer.apply(obs); + let l2 = this.secondLayer.apply(l1); + + return this.outputLayer.apply(l2); + }); + } + const output = this.predict(); + this.model = tf.model({inputs: obs, outputs: output}); + } +}; + +class Critic { + + /** + * @param config (Object) + */ + constructor(config) { + this.stateSize = config.stateSize; + this.nbActions = config.nbActions; + this.layerNorm = config.layerNorm; + + this.firstLayerSSize = config.criticFirstLayerSSize + this.firstLayerASize = config.criticFirstLayerASize; + this.secondLayerSize = config.criticSecondLayerSize; + + this.seed = config.seed; + this.config = config; + this.obs = null; + this.action = null; + } + + /** + * + * @param obs tf.input + * @param action tf.input + */ + buildModel(obs, action){ + this.obs = obs; + this.action = action; + + // Used to merged the two first Layer later. + this.add = tf.layers.add(); + + // First layer + this.firstLayerS = tf.layers.dense({ + units: this.firstLayerSSize, + kernelInitializer: tf.initializers.glorotUniform({seed: this.seed}), + activation: 'linear', // relu is add later + useBias: true, + biasInitializer: "zeros" + }); + // First layer + this.firstLayerA = tf.layers.dense({ + units: this.firstLayerASize, + kernelInitializer: tf.initializers.glorotUniform({seed: this.seed}), + activation: 'linear', // relu is add later + useBias: true, + biasInitializer: "zeros" + }); + // Second layer + this.secondLayer = tf.layers.dense({ + units: this.secondLayerSize, + kernelInitializer: tf.initializers.glorotUniform({seed: this.seed}), + activation: 'relu', + useBias: true, + biasInitializer: "zeros" + }); + + // Ouput layer + this.outputLayer = tf.layers.dense({ + units: 1, + kernelInitializer: tf.initializers.randomUniform({ + minval: 0.003, maxval: 0.003, seed: this.seed}), + activation: 'linear', + useBias: true, + biasInitializer: "zeros" + }); + + // Critic prediction + this.predict = (tfState, tfActions) => { + return tf.tidy(() => { + if (tfState && tfActions){ + obs = tfState; + action = tfActions; + } + + let l1A = this.firstLayerA.apply(action); + let l1S = this.firstLayerS.apply(obs) + // Merged layers + let concat = this.add.apply([l1A, l1S]) + + let l2 = this.secondLayer.apply(concat); + + return this.outputLayer.apply(l2); + }); + } + + const output = this.predict(); + this.model = tf.model({inputs: [obs, action], outputs: output}); + } +}; diff --git a/demo/webapp/public/js/ddpg/noise.js b/demo/webapp/public/js/ddpg/noise.js new file mode 100644 index 0000000..08c8978 --- /dev/null +++ b/demo/webapp/public/js/ddpg/noise.js @@ -0,0 +1,41 @@ +/** + * Noise class + * The original baseline is made of three noise + * AdaptiveParamNoiseSpec, ActionNoise and NormalActionNoise + * Only AdaptiveParamNoiseSpec is implemented for now + * See "C Adapative Scaling" Page 14 in the paper. + */ + +class AdaptiveParamNoiseSpec { + + /** + * @param conf Object + * conf.initialStddev: 0.1 default // σ + * conf.desiredActionStddev: 0.1 default // δ + * conf.adoptionCoefficient: 1.01 default // α + */ + constructor(conf){ + conf = conf || {}; + this.initialStddev = conf.initialStddev || 0.4; + this.desiredActionStddev = conf.desiredActionStddev || 0.4; + this.adoptionCoefficient = conf.adoptionCoefficient || 1.01; + this.currentStddev = this.initialStddev; + } + + /** + * The distance from the Adaptive scaling + * @param distance number + */ + adapt(distance){ + // if d(π, _π_) > δ then σ = σ/α + if (distance > this.desiredActionStddev){ + // Decrease σ + this.currentStddev /= this.adoptionCoefficient; + } + else{ + // σ = σ*α + // Increase σ + this.currentStddev *= this.adoptionCoefficient; + } + } +}; \ No newline at end of file diff --git a/demo/webapp/public/js/ddpg/prioritized_memory.js b/demo/webapp/public/js/ddpg/prioritized_memory.js new file mode 100644 index 0000000..5a7f74b --- /dev/null +++ b/demo/webapp/public/js/ddpg/prioritized_memory.js @@ -0,0 +1,240 @@ + +class PrioritizedMemory { + + /** + * @param maxlen (number) Buffer limit + */ + constructor(maxlen){ + this.maxlen = maxlen; + this.buffer = []; + this.priorBuffer = []; + } + + /** + * Sample a batch + * @param batchSize (number) + * @return batch [] + */ + getBatch(batchSize){ + const batch = { + 'obs0': [], + 'obs1': [], + 'rewards': [], + 'actions': [], + 'terminals': [], + }; + + if (batchSize > this.priorBuffer.length){ + console.warn("The size of the replay buffer is < to the batchSize. Return empty batch."); + return batch; + } + + for (let b=0; b < batchSize/2; b++){ + let id = Math.floor(Math.random() * this.priorBuffer.length); + batch.obs0.push(this.priorBuffer[id].obs0); + batch.obs1.push(this.priorBuffer[id].obs1); + batch.rewards.push(this.priorBuffer[id].reward); + batch.actions.push(this.priorBuffer[id].action); + batch.terminals.push(this.priorBuffer[id].terminal); + } + return batch + } + + _bufferBatch(batchSize){ + const batch = { + 'obs0': [], + 'obs1': [], + 'rewards': [], + 'actions': [], + 'terminals': [], + }; + + for (let b=0; b < batchSize/2; b++){ + let nElem = this.buffer.pop(); + batch.obs0.push(nElem.obs0); + batch.obs1.push(nElem.obs1); + batch.rewards.push(nElem.reward); + batch.actions.push(nElem.action); + batch.terminals.push(nElem.terminal); + } + + for (let b=0; b < batchSize/2; b++){ + let id = Math.floor(Math.random() * this.buffer.length); + batch.obs0.push(this.buffer[id].obs0); + batch.obs1.push(this.buffer[id].obs1); + batch.rewards.push(this.buffer[id].reward); + batch.actions.push(this.buffer[id].action); + batch.terminals.push(this.buffer[id].terminal); + this.buffer.splice(id, 1); + } + + return batch + } + + _addRandomBufferBatch(batchSize, batch){ + for (let b=0; b < batchSize; b++){ + let id = Math.floor(Math.random() * this.buffer.length); + batch.obs0.push(this.buffer[id].obs0); + batch.obs1.push(this.buffer[id].obs1); + batch.rewards.push(this.buffer[id].reward); + batch.actions.push(this.buffer[id].action); + batch.terminals.push(this.buffer[id].terminal); + this.buffer.splice(id, 1); + } + return batch + } + + /** + * Sample a batch + * @param batchSize (number) + * @return batch [] + */ + popBatch(batchSize){ + let originalBatchSize = batchSize; + let priorBufferBatchSize; + let bufferBatchSize; + if (batchSize % 2 != 0){ + console.warn("Batch size should be a even.") + } + if (this.priorBuffer.length < batchSize/2){ + //console.log("get full batch from buffer"); + const batch = this._bufferBatch(batchSize); + console.assert(batch.obs0.length == batchSize); + return batch; + } + const batch = { + 'obs0': [], + 'obs1': [], + 'rewards': [], + 'actions': [], + 'terminals': [], + }; + if (batchSize > this.length){ + console.warn("The size of the replay buffer is < to the batchSize. Return empty batch."); + return batch; + } + + if (this.buffer.length > 0){ + //console.log("Get half of prior and other from buffer."); + batchSize = batchSize / 2; + } + else{ + //console.log("Get all from priorBuffer"); + } + + for (let b=0; b < batchSize; b++){ + let id = Math.floor(Math.random() * this.priorBuffer.length); + batch.obs0.push(this.priorBuffer[id].obs0); + batch.obs1.push(this.priorBuffer[id].obs1); + batch.rewards.push(this.priorBuffer[id].reward); + batch.actions.push(this.priorBuffer[id].action); + batch.terminals.push(this.priorBuffer[id].terminal); + this.priorBuffer.splice(id, 1); + } + + if (this.buffer.length > 0){ + this._addRandomBufferBatch(batchSize, batch); + } + console.assert(batch.obs0.length == originalBatchSize); + return batch + } + + _insert(element, array) { + if (array.length == 0 || element.cost < array[0].cost || array[0].cost == null){ + array.unshift(element); + return array; + } + array.splice(this._locationOf(element, array) + 1, 0, element); + return array; + } + + _locationOf(element, array, start, end) { + start = start || 0; + end = end || array.length; + + var pivot = parseInt(start + (end - start) / 2, 10); + + if (end-start <= 1 || array[pivot] === element) return pivot; + + if (array[pivot].cost != null && array[pivot].cost < element.cost) { + return this._locationOf(element, array, pivot, end); + } else { + return this._locationOf(element, array, start, pivot); + } + } + + /** + * @param batch (Object) from getBatch() + * @param cost (number) Cost associated with each row of the batch + */ + appendBackWithCost(batch, costs){ + for (let b=0; b < batch.obs0.length; b++){ + if (this.buffer.length == this.maxlen){ + this.buffer.shift(); + } + this._insert({ + obs0: batch.obs0[b], + action: batch.actions[b], + reward: batch.rewards[b], + obs1: batch.obs1[b], + terminal: batch.terminals[b], + cost: costs[b] + }, this.buffer); + } + console.assert(this.buffer.length <= this.maxlen); + } + + /** + * @param obs0 [] + * @param action (number) + * @param reward (number) + * @param obs1 [] + * @param terminal1 (boolean) + */ + append(obs0, action, reward, obs1, terminal){ + if (this.priorBuffer.length == this.maxlen){ + this.priorBuffer.shift(); + } + this.priorBuffer.push({ + obs0: obs0, + action: action, + reward: reward, + obs1: obs1, + terminal: terminal, + cost: null + }); + console.assert(this.priorBuffer.length <= this.maxlen); + } +} +/* +var mem = new Memory(20000); +Math.seedrandom(0); +console.assert(mem.length == 0); + +var array = []; +for (let i=1; i < 40000; i++){ + mem.append("obs0-"+i, "action-"+i, "reward-"+i, "obs1-"+i, "terminal-"+i); +} + +console.assert(mem.length == 20000); +console.assert(mem.list[0].obs0 == "obs0-20000"); +console.assert(mem.list[19999].obs0 == "obs0-39999"); + +let batch = mem.getBatch(32); + +console.assert(batch.obs0.length == 32); +console.assert(mem.length == 20000 - 32); + +let costs = []; +for (i=31; i >= 0; i--){ + costs.push(i); +} +mem.appendBackWithCost(batch, costs); + +console.log(mem.list); + +/* +for (let i=1; i < 64; i++){ + mem.append("obs0-"+i, "action-"+i, "reward-"+i, "obs1-"+i, "terminal-"+i); +} +*/ \ No newline at end of file diff --git a/demo/webapp/public/js/editor.js b/demo/webapp/public/js/editor.js index 97b0e54..3a569b3 100644 --- a/demo/webapp/public/js/editor.js +++ b/demo/webapp/public/js/editor.js @@ -16,5 +16,5 @@ editor.load().then(() => { localStorage.setItem('mylevel.json', JSON.stringify(content)); window.open("/test_editor.html"); - }, {download: false, name: "level.json"}); + }, {download: true, name: "level.json"}); }); \ No newline at end of file diff --git a/demo/webapp/public/js/level2.js b/demo/webapp/public/js/level2.js deleted file mode 100644 index 5b1b5b7..0000000 --- a/demo/webapp/public/js/level2.js +++ /dev/null @@ -1,14 +0,0 @@ -let levelUrl = metacar.level.level2; - -var env = new metacar.env("canvas", levelUrl); - -env.setAgentMotion(metacar.motion.ControlMotion, {}); - -env.loop(() => { - let state = env.getState(); - displayState("realtime_viewer", state, 200, 200); - let reward = env.getLastReward(); - displayScores("realtime_viewer", [], reward, []); -}); - -env.load(); \ No newline at end of file diff --git a/demo/webapp/public/js/level1.js b/demo/webapp/public/js/policy_monte_carlo/index.js similarity index 94% rename from demo/webapp/public/js/level1.js rename to demo/webapp/public/js/policy_monte_carlo/index.js index 513f8de..b8b5632 100644 --- a/demo/webapp/public/js/level1.js +++ b/demo/webapp/public/js/policy_monte_carlo/index.js @@ -7,7 +7,7 @@ var env = new metacar.env("canvas", levelUrl); var agent = new PolicyAgent(env); env.loop(() => { - let state = env.getState(); + let state = env.getState().lidar; displayState("realtime_viewer", state, 200, 200); let scores = agent.getStateValues(state); let reward = env.getLastReward(); @@ -30,6 +30,6 @@ env.load().then(() => { env.addEvent("save", () => agent.save()); env.addEvent("load", () => { document.getElementById("metacar_canvas_button_train").style.display = "none"; - agent.restore() + agent.restore(); }); }); diff --git a/demo/webapp/public/js/policy_agent.js b/demo/webapp/public/js/policy_monte_carlo/policy_agent.js similarity index 95% rename from demo/webapp/public/js/policy_agent.js rename to demo/webapp/public/js/policy_monte_carlo/policy_agent.js index 08cc158..5f2d2fd 100644 --- a/demo/webapp/public/js/policy_agent.js +++ b/demo/webapp/public/js/policy_monte_carlo/policy_agent.js @@ -179,14 +179,12 @@ class PolicyAgent { */ this.valueModel = await tf.loadModel('https://metacar-project.com/public/models/policy/value-model-policy-agent.json'); this.policyModel = await tf.loadModel("https://metacar-project.com/public/models/policy/policy-model-policy-agent.json"); - //this.valueModel = await tf.loadModel('http://localhost:3000/public/models/policy/value-model-policy-agent.json'); - //this.policyModel = await tf.loadModel("http://localhost:3000/public/models/policy/policy-model-policy-agent.json"); } play(){ tf.tidy(() => { // Get the current state - const st = tf.tensor2d(this.env.getState(), [this.lidarPts, this.lidarPts]).reshape([1, this.ttLidarPts]); + const st = tf.tensor2d(this.env.getState().lidar, [this.lidarPts, this.lidarPts]).reshape([1, this.ttLidarPts]); // Predict the policy const softmax = this.policyModel.predict(st); // Get the action @@ -231,7 +229,8 @@ class PolicyAgent { console.time("Exploring"); for (var step = 0; step < this.nb_step; step++) { // Get the current state - const array_st = this.env.getState(true); + let array_st = this.env.getState().linear; + array_st = array_st.slice(0, array_st.length - 1); // Convert the state into a tensor //const st = tf.tensor(array_st, [this.lidarPts, this.lidarPts]).reshape([1, this.ttLidarPts]); const st = tf.tensor2d([array_st]); @@ -301,7 +300,7 @@ class PolicyAgent { tf_advantages.dispose(); // Set the agent on a new free road - this.env.randomRoadPosition(); + this.env.shuffle({cars: false}); //env.reset(); // Go to the next episode setTimeout(() => this.train(this.env, it+1), 1); diff --git a/demo/webapp/public/js/level0.js b/demo/webapp/public/js/q_table/index.js similarity index 92% rename from demo/webapp/public/js/level0.js rename to demo/webapp/public/js/q_table/index.js index 4e44e77..4e6f42b 100644 --- a/demo/webapp/public/js/level0.js +++ b/demo/webapp/public/js/q_table/index.js @@ -12,8 +12,8 @@ var agent = new QTableAgent(env, 2); env.loop(() => { let state = env.getState(); - displayState("realtime_viewer", state, 200, 200); - let scores = agent.getStateValues(state); + displayState("realtime_viewer", state.lidar, 200, 200); + let scores = agent.getStateValues(state.lidar); let reward = env.getLastReward(); displayScores("realtime_viewer", scores, reward, ["Top", "Left", "Right"]); }); diff --git a/demo/webapp/public/js/q_table_agent.js b/demo/webapp/public/js/q_table/q_table_agent.js similarity index 95% rename from demo/webapp/public/js/q_table_agent.js rename to demo/webapp/public/js/q_table/q_table_agent.js index 9a96e22..58c532b 100644 --- a/demo/webapp/public/js/q_table_agent.js +++ b/demo/webapp/public/js/q_table/q_table_agent.js @@ -54,7 +54,7 @@ class QTableAgent { play(){ // Get the current state - let state = this.env.getState(); + let state = this.env.getState().lidar; state = state.toString(); // In this state in not in the Q(s, a) function if (!(state in this.Q)){ @@ -108,7 +108,7 @@ class QTableAgent { console.log("episode=", ep, "eps=", eps, "mean_reward", mean(mean_reward)); } mean_reward = []; - let st = this.env.getState().toString(); + let st = this.env.getState().lidar.toString(); let act; let gamma = 0.99; let st2; @@ -117,7 +117,7 @@ class QTableAgent { act = this.pickAction(st, eps); let reward = this.env.step(act); mean_reward.push(reward); - st2 = this.env.getState().toString(); + st2 = this.env.getState().lidar.toString(); // Pick greedy action (eps = 0) act2 = this.pickAction(st2, 0.); this.createStateIfNotExist(st2); @@ -125,7 +125,7 @@ class QTableAgent { this.Q[st][act] = this.Q[st][act] + 0.01*(reward + (gamma*this.Q[st2][act2]) - this.Q[st][act]); st = st2; } - this.env.randomRoadPosition(); + this.env.shuffle({cars: false}); } this.env.render(true); for (let s=0; s < this.stateList.length; s++){ diff --git a/demo/webapp/public/js/viewer.js b/demo/webapp/public/js/viewer.js index dba4376..6994e0a 100644 --- a/demo/webapp/public/js/viewer.js +++ b/demo/webapp/public/js/viewer.js @@ -112,4 +112,51 @@ function displayState(id, state, width, height){ yPos += ySize; } +} + +METRICS = {}; + +function initMetricsContainer(container, metrics){ + container = document.getElementById(container); + for (let m=0; m < metrics.length; m++){ + nDiv = document.createElement("div"); + nDiv.id = 'metrics_'+metrics[m]; + nDiv.style.width = "250px"; + nDiv.style.height = "250px"; + nDiv.style.display = "inline-block"; + nDiv.style.marginRight = "10px"; + container.appendChild(nDiv); + + METRICS[metrics[m]] = new CanvasJS.Chart('metrics_'+metrics[m], { + width: 250, + height: 250, + animationEnabled: false, + theme: "light2", + title:{ + text: metrics[m], + }, + axisY:{ + includeZero: false + }, + data: [{ + type: "line", + dataPoints: [{y: 0}] + }] + }); + METRICS[metrics[m]].render(); + } +} + +function setMetric(name, value){ + let chart = METRICS[name]; + let size = chart.options.data[0].dataPoints.length - 1; + + if (chart.options.data[0].dataPoints.length > 500){ + chart.options.data[0].dataPoints = chart.options.data[0].dataPoints.slice(1, size); + size = size - 1; + } + + size = chart.options.data[0].dataPoints.length - 1; + chart.options.data[0].dataPoints.push({y: value, x: chart.options.data[0].dataPoints[size].x+1}); + chart.render(); } \ No newline at end of file diff --git a/demo/webapp/public/models/ddpg-road/actor-model-ddpg-road.json b/demo/webapp/public/models/ddpg-road/actor-model-ddpg-road.json new file mode 100644 index 0000000..f5fb493 --- /dev/null +++ b/demo/webapp/public/models/ddpg-road/actor-model-ddpg-road.json @@ -0,0 +1 @@ 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0000000..55cff60 --- /dev/null +++ b/demo/webapp/public/models/ddpg-road/critic-model-ddpg-road.json @@ -0,0 +1 @@ +{"modelTopology":{"class_name":"Model","config":{"name":"model2","layers":[{"name":"input2","class_name":"InputLayer","config":{"batch_input_shape":[null,2],"dtype":"float32","sparse":false,"name":"input2"},"inbound_nodes":[]},{"name":"input1","class_name":"InputLayer","config":{"batch_input_shape":[null,26],"dtype":"float32","sparse":false,"name":"input1"},"inbound_nodes":[]},{"name":"dense_Dense5","class_name":"Dense","config":{"units":64,"activation":"linear","use_bias":true,"kernel_initializer":{"class_name":"VarianceScaling","config":{"scale":1,"mode":"fan_avg","distribution":"uniform","seed":0}},"bias_initializer":{"class_name":"Zeros","config":{}},"kernel_regularizer":null,"bias_regularizer":null,"activity_regularizer":null,"kernel_constraint":null,"bias_constraint":null,"name":"dense_Dense5","trainable":true},"inbound_nodes":[[["input2",0,0,{}]]]},{"name":"dense_Dense4","class_name":"Dense","config":{"units":64,"activation":"linear","use_bias":true,"kernel_initializer":{"class_name":"VarianceScaling","config":{"scale":1,"mode":"fan_avg","distribution":"uniform","seed":0}},"bias_initializer":{"class_name":"Zeros","config":{}},"kernel_regularizer":null,"bias_regularizer":null,"activity_regularizer":null,"kernel_constraint":null,"bias_constraint":null,"name":"dense_Dense4","trainable":true},"inbound_nodes":[[["input1",0,0,{}]]]},{"name":"add_Add1","class_name":"Add","config":{"name":"add_Add1","trainable":true},"inbound_nodes":[[["dense_Dense5",0,0,{}],["dense_Dense4",0,0,{}]]]},{"name":"dense_Dense6","class_name":"Dense","config":{"units":32,"activation":"relu","use_bias":true,"kernel_initializer":{"class_name":"VarianceScaling","config":{"scale":1,"mode":"fan_avg","distribution":"uniform","seed":0}},"bias_initializer":{"class_name":"Zeros","config":{}},"kernel_regularizer":null,"bias_regularizer":null,"activity_regularizer":null,"kernel_constraint":null,"bias_constraint":null,"name":"dense_Dense6","trainable":true},"inbound_nodes":[[["add_Add1",0,0,{}]]]},{"name":"dense_Dense7","class_name":"Dense","config":{"units":1,"activation":"linear","use_bias":true,"kernel_initializer":{"class_name":"RandomUniform","config":{"minval":0.003,"maxval":0.003,"seed":0}},"bias_initializer":{"class_name":"Zeros","config":{}},"kernel_regularizer":null,"bias_regularizer":null,"activity_regularizer":null,"kernel_constraint":null,"bias_constraint":null,"name":"dense_Dense7","trainable":true},"inbound_nodes":[[["dense_Dense6",0,0,{}]]]}],"input_layers":[["input1",0,0],["input2",0,0]],"output_layers":[["dense_Dense7",0,0]]},"keras_version":"tfjs-layers 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a/demo/webapp/public/models/ddpg-traffic/actor-model-ddpg-traffic-epoch-120.json b/demo/webapp/public/models/ddpg-traffic/actor-model-ddpg-traffic-epoch-120.json new file mode 100644 index 0000000..728c09d --- /dev/null +++ b/demo/webapp/public/models/ddpg-traffic/actor-model-ddpg-traffic-epoch-120.json @@ -0,0 +1 @@ +{"modelTopology":{"class_name":"Model","config":{"name":"model7","layers":[{"name":"input1","class_name":"InputLayer","config":{"batch_input_shape":[null,50],"dtype":"float32","sparse":false,"name":"input1"},"inbound_nodes":[]},{"name":"dense_Dense22","class_name":"Dense","config":{"units":128,"activation":"relu","use_bias":true,"kernel_initializer":{"class_name":"VarianceScaling","config":{"scale":1,"mode":"fan_avg","distribution":"uniform","seed":0}},"bias_initializer":{"class_name":"Zeros","config":{}},"kernel_regularizer":null,"bias_regularizer":null,"activity_regularizer":null,"kernel_constraint":null,"bias_constraint":null,"name":"dense_Dense22","trainable":true},"inbound_nodes":[[["input1",0,0,{}]]]},{"name":"dense_Dense23","class_name":"Dense","config":{"units":64,"activation":"relu","use_bias":true,"kernel_initializer":{"class_name":"VarianceScaling","config":{"scale":1,"mode":"fan_avg","distribution":"uniform","seed":0}},"bias_initializer":{"class_name":"Zeros","config":{}},"kernel_regularizer":null,"bias_regularizer":null,"activity_regularizer":null,"kernel_constraint":null,"bias_constraint":null,"name":"dense_Dense23","trainable":true},"inbound_nodes":[[["dense_Dense22",0,0,{}]]]},{"name":"dense_Dense24","class_name":"Dense","config":{"units":2,"activation":"tanh","use_bias":true,"kernel_initializer":{"class_name":"RandomUniform","config":{"minval":0.003,"maxval":0.003,"seed":0}},"bias_initializer":{"class_name":"Zeros","config":{}},"kernel_regularizer":null,"bias_regularizer":null,"activity_regularizer":null,"kernel_constraint":null,"bias_constraint":null,"name":"dense_Dense24","trainable":true},"inbound_nodes":[[["dense_Dense23",0,0,{}]]]}],"input_layers":[["input1",0,0]],"output_layers":[["dense_Dense24",0,0]]},"keras_version":"tfjs-layers 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b/demo/webapp/public/models/ddpg-traffic/critic-model-ddpg-traffic-epoch-120.json new file mode 100644 index 0000000..2f54e16 --- /dev/null +++ b/demo/webapp/public/models/ddpg-traffic/critic-model-ddpg-traffic-epoch-120.json @@ -0,0 +1 @@ +{"modelTopology":{"class_name":"Model","config":{"name":"model6","layers":[{"name":"input2","class_name":"InputLayer","config":{"batch_input_shape":[null,2],"dtype":"float32","sparse":false,"name":"input2"},"inbound_nodes":[]},{"name":"input1","class_name":"InputLayer","config":{"batch_input_shape":[null,50],"dtype":"float32","sparse":false,"name":"input1"},"inbound_nodes":[]},{"name":"dense_Dense19","class_name":"Dense","config":{"units":128,"activation":"linear","use_bias":true,"kernel_initializer":{"class_name":"VarianceScaling","config":{"scale":1,"mode":"fan_avg","distribution":"uniform","seed":0}},"bias_initializer":{"class_name":"Zeros","config":{}},"kernel_regularizer":null,"bias_regularizer":null,"activity_regularizer":null,"kernel_constraint":null,"bias_constraint":null,"name":"dense_Dense19","trainable":true},"inbound_nodes":[[["input2",0,0,{}]]]},{"name":"dense_Dense18","class_name":"Dense","config":{"units":128,"activation":"linear","use_bias":true,"kernel_initializer":{"class_name":"VarianceScaling","config":{"scale":1,"mode":"fan_avg","distribution":"uniform","seed":0}},"bias_initializer":{"class_name":"Zeros","config":{}},"kernel_regularizer":null,"bias_regularizer":null,"activity_regularizer":null,"kernel_constraint":null,"bias_constraint":null,"name":"dense_Dense18","trainable":true},"inbound_nodes":[[["input1",0,0,{}]]]},{"name":"add_Add3","class_name":"Add","config":{"name":"add_Add3","trainable":true},"inbound_nodes":[[["dense_Dense19",0,0,{}],["dense_Dense18",0,0,{}]]]},{"name":"dense_Dense20","class_name":"Dense","config":{"units":64,"activation":"relu","use_bias":true,"kernel_initializer":{"class_name":"VarianceScaling","config":{"scale":1,"mode":"fan_avg","distribution":"uniform","seed":0}},"bias_initializer":{"class_name":"Zeros","config":{}},"kernel_regularizer":null,"bias_regularizer":null,"activity_regularizer":null,"kernel_constraint":null,"bias_constraint":null,"name":"dense_Dense20","trainable":true},"inbound_nodes":[[["add_Add3",0,0,{}]]]},{"name":"dense_Dense21","class_name":"Dense","config":{"units":1,"activation":"linear","use_bias":true,"kernel_initializer":{"class_name":"RandomUniform","config":{"minval":0.003,"maxval":0.003,"seed":0}},"bias_initializer":{"class_name":"Zeros","config":{}},"kernel_regularizer":null,"bias_regularizer":null,"activity_regularizer":null,"kernel_constraint":null,"bias_constraint":null,"name":"dense_Dense21","trainable":true},"inbound_nodes":[[["dense_Dense20",0,0,{}]]]}],"input_layers":[["input1",0,0],["input2",0,0]],"output_layers":[["dense_Dense21",0,0]]},"keras_version":"tfjs-layers 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b/demo/webapp/public/models/ddpg-traffic/critic-model-ddpg-traffic-epoch-120.weights.bin differ diff --git a/demo/webapp/level0.html b/demo/webapp/qtable.html similarity index 89% rename from demo/webapp/level0.html rename to demo/webapp/qtable.html index 693a8b5..2b1a82b 100644 --- a/demo/webapp/level0.html +++ b/demo/webapp/qtable.html @@ -2,7 +2,7 @@ - Metacar: Q-learning demo + Metacar: Discrete Actions. @@ -25,7 +25,7 @@

-

Current state (Lidar points)


+

Current state (Lidar points)



Q-learning algorithm with table.
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o=Math.abs((this.car.next_road.outroad.y+d/2-this.car.y)*this.car.next_road.outroad_np.my),n=Math.abs((this.car.next_road.outroad.x+d/2-this.car.x)*this.car.next_road.outroad_np.mx);if(0==this.car.next_road.rotation_type&&othis.car.next_road.outroad.x+d/2?(this.car.x=this.car.next_road.outroad.x+d-d/2-d/4,this.car.y=this.car.next_road.outroad.y):0!=n&&this.car.ythis.car.next_road.outroad.y+d/2&&(this.car.y=this.car.next_road.outroad.y+d-d/2-d/4,this.car.x=this.car.next_road.outroad.x),void(this.car.next_road=void 0)}else{var 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r=-t.orientation*Math.PI,s=0*Math.cos(r)-a*Math.sin(r),h=Math.cos(r)*a+0*Math.sin(r),c=0==i?1:-1,l=0==i?0:Math.PI;e.x+=c*Math.round(s),e.y+=c*Math.round(h),e.mx=Math.floor(e.x/d),e.my=Math.floor(e.y/d),e.rotation=r+l,e.checkAndsetNewRoad(t)},t.prototype.createAsset=function(t,e,i,o){var n=new a(i[t]);n.obstacle=!1,n.type=o,n.mapId=0,n.x=e.x,n.y=e.y,this.level.addChild(n),this.assets.push(n)},t.prototype.createMap=function(t,e,i,o){if(void 0===o&&(o=!0),o)for(var n=0;n0?o=-1:e||(o=-1),o},e.prototype.getLastReward=function(){return this.lastReward},e.prototype.step=function(t,e){void 0===e&&(e=null);for(var 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0,this.core.carId=C,C+=1,this.core.agent=!1,this.core.obstacle=!0,this.core.mapId=y.CAR,o.lidar&&(this.createLidar(o.lidarInfo),this.core.lidar=this.lidar),o.motionEngine?(this.motion=o.motionEngine,this.motion.setUp(this.core,this.lidar),this.core.rotationStep=.5):this.core.rotationStep=.5,this.core.mx=this.info.mx,this.core.my=this.info.my,this.core.x=this.info.mx*d,this.core.y=this.info.my*d,this.core.checkAndsetNewRoad=function(t){return n.checkAndsetNewRoad(t)};var r=this.level.getRoad(this.core.my,this.core.mx);this.core.line=0,r&&r.setCarPosition(this.core,this.info.line)}return t.prototype.checkAndsetNewRoad=function(t){var e=this.core.road;t||(t=this.level.getRoad(this.core.my,this.core.mx)),t!=e&&(e&&e.cars.splice(e.cars.indexOf(this.core.carId),1),this.core.road=t,this.core.haveTurned=!1,this.core.optionalTurn=!1,this.core.turnedRandom=void 0,this.core.next_road=!1,t&&t.cars.push(this.core.carId))},t.prototype.reset=function(){this.core.mx=this.info.mx,this.core.my=this.info.my;var t=this.level.getRoad(this.core.my,this.core.mx);t&&t.setCarPosition(this.core,this.info.line,!0)},t.prototype.getState=function(){var t={},e=[];return this.motion.state.map(function(t){e=e.concat(t)}),e.push(this.core.v),t.linear=e,t.lidar=this.motion.state.map(function(t){return t.slice()}),t.v=this.core.v,t.a=this.core.last_a,t.steering=this.core.last_yaw_rate,t},t.prototype.step=function(t,e){if(void 0===e&&(e=null),null==e)var i=this.motion.step(t),o=i.agentCollisions,n=i.onRoad;else{var a=this.motion.actionStep(t,e);o=a.agentCollisions,n=a.onRoad}return{agentCollisions:o,onRoad:n}},t.prototype.createLidar=function(t){t.pts=t.pts||5,t.width=t.width||4,t.height=t.height||5,t.pos=t.pos||0,this.lidar=new s,this.lidar.lidarPts=[],this.lidar.collisionPts=[];var e=new r;e.pt=!1,e.alpha=.3,e.beginFill(5329233),e.drawRect(0,0,this.core.width*t.width,this.core.height*t.height),e.endFill();var i=new r;i.pt=!1,i.alpha=0,i.beginFill(5308416),i.drawRect(this.core.width-5,e.height/2,3,3),i.endFill();var o=new r;o.pt=!1,o.alpha=0,o.beginFill(5308416),o.drawRect(-1,e.height/2-2,3,3),o.endFill();var n=new r;n.pt=!1,n.alpha=0,n.beginFill(5308416),n.drawRect(this.core.width/2,e.height/2-this.core.height/2+5,3,3),n.endFill();var a=new r;a.pt=!1,a.alpha=0,a.beginFill(5308416),a.drawRect(this.core.width/2,e.height/2+this.core.height/2-8,3,3),a.endFill(),o.x-=t.pos*this.core.width,i.x-=t.pos*this.core.width,a.x-=t.pos*this.core.width,n.x-=t.pos*this.core.width;for(var h=e.width/t.pts,c=e.height/t.pts,l=t.pts-1;l>=0;l--)for(var d=0;do.x&&i.xo.y&&i.y=4||!t)&&(r=!0),{agentCollisions:o,onRoad:r}},t.prototype.setState=function(t){for(var e=0,i=0;ithis.state[o][n]||t[a].mapId==y.ROAD&&this.state[o][n]==y.DEFAULT)&&(this.state[o][n]=t[a].mapId)}e+=1}},t}(),b=(I=Object.setPrototypeOf||{__proto__:[]}instanceof Array&&function(t,e){t.__proto__=e}||function(t,e){for(var i in e)e.hasOwnProperty(i)&&(t[i]=e[i])},function(t,e){function i(){this.constructor=t}I(t,e),t.prototype=null===e?Object.create(e):(i.prototype=e.prototype,new i)}),R=function(t){function e(e,i){var o=t.call(this,e)||this;return o.maxSpeed=1,o.rotationStep=i.rotationStep||.5,o.actions=i.actions||["UP","LEFT","RIGHT","DOWN","WAIT"],o}return b(e,t),e.prototype.setUp=function(t,e){this.car=t,this.lidar=e,this.state=[];for(var i=0;i0&&(this.car.v=0,this.car.vy=0),{agentCollisions:i,onRoad:o}},e}(M),E=function(){var t=Object.setPrototypeOf||{__proto__:[]}instanceof Array&&function(t,e){t.__proto__=e}||function(t,e){for(var i in e)e.hasOwnProperty(i)&&(t[i]=e[i])};return function(e,i){function o(){this.constructor=e}t(e,i),e.prototype=null===i?Object.create(i):(o.prototype=i.prototype,new o)}}(),S=function(t){function e(e){var i=t.call(this,e)||this;return i.map=i.level.getMap(),i.mapSizeY=i.map.length,i.mapSizeX=i.map[0].length,i.rotationStep=.5,i.rotationToNextCase={},i.rotationToNextCase[0]={mx:1,my:0},i.rotationToNextCase[.5]={mx:0,my:1},i.rotationToNextCase[1]={mx:-1,my:0},i.rotationToNextCase[1.5]={mx:0,my:-1},i.rotationToNextCase[-.5]={mx:0,my:-1},i.rotationToNextCase[-1]={mx:-1,my:0},i.rotationToNextCase[-1.5]={mx:0,my:1},i}return E(e,t),e.prototype.setUp=function(t,e){this.car=t,this.lidar=e,this.state=[];for(var i=0;i=this.mapSizeY||t<0||t>=this.mapSizeX||0==this.map[e][t])return!1;if(i&&this.level.getRoad(e,t).cars.length>0)return!1;return!0},e.prototype.autoRotation=function(){var t=d+d/3,e=d-d/3.5,i=d;if(this.car.next_road){var o=Math.abs((this.car.next_road.outroad.y+d/2-this.car.y)*this.car.next_road.outroad_np.my),n=Math.abs((this.car.next_road.outroad.x+d/2-this.car.x)*this.car.next_road.outroad_np.mx);if(0==this.car.next_road.rotation_type&&othis.car.next_road.outroad.x+d/2?(this.car.x=this.car.next_road.outroad.x+d-d/2-d/4,this.car.y=this.car.next_road.outroad.y):0!=n&&this.car.ythis.car.next_road.outroad.y+d/2&&(this.car.y=this.car.next_road.outroad.y+d-d/2-d/4,this.car.x=this.car.next_road.outroad.x),void(this.car.next_road=void 0)}else{var a=this.car.rotation/Math.PI,r=(this.car.rotation/Math.PI+.5)%2,s=(this.car.rotation/Math.PI-.5)%2,h=this.rotationToNextCase[a],c=this.rotationToNextCase[r],l=this.rotationToNextCase[s],p=!1,y=this.car.mx+h.mx,u=this.car.my+h.my;if(this.isRoad(y,u)?this.car.haveTurned||this.car.turnedRandom?!this.car.haveTurned&&this.car.turnedRandom<.5&&this.isRoad(this.car.mx+c.mx,this.car.my+c.my)?(p=!0,this.car.optionalTurn=!0):!this.car.haveTurned&&this.car.turnedRandom>=.5&&this.isRoad(this.car.mx+l.mx,this.car.my+l.my)&&(p=!0,this.car.optionalTurn=!0):this.car.turnedRandom=Math.random():(p=!0,this.car.optionalTurn=!1),p)for(var x=[(a+.5)%2,(a-.5)%2,null],v=0;v=1&&n&&n.core.line==i&&(i=0==i?1:0),e.line=i,e.a=0,e.v=0,e.x=t.x,e.y=t.y,e.x+=d/2,e.y+=d/2;var a=1*d/4;t.orientation=Math.floor(t.orientation/e.rotationStep)*e.rotationStep;var r=-t.orientation*Math.PI,s=0*Math.cos(r)-a*Math.sin(r),h=Math.cos(r)*a+0*Math.sin(r),c=0==i?1:-1,l=0==i?0:Math.PI;e.x+=c*Math.round(s),e.y+=c*Math.round(h),e.mx=Math.floor(e.x/d),e.my=Math.floor(e.y/d),e.rotation=r+l,e.checkAndsetNewRoad(t)},t.prototype.createAsset=function(t,e,i,o){var n=new a(i[t]);n.obstacle=!1,n.type=o,n.mapId=0,n.x=e.x,n.y=e.y,this.level.addChild(n),this.assets.push(n)},t.prototype.createMap=function(t,e,i,o){if(void 0===o&&(o=!0),o)for(var n=0;n0?o=-1:e||(o=-1),o},e.prototype.getLastReward=function(){return this.lastReward},e.prototype.step=function(t,e,i){if(void 0===e&&(e=null),void 0===i&&(i=!0),!i||this.steping){for(var o=0;o0&&0==this.car.a?this.car.v=Math.max(0,this.car.v-.01):this.car.v<0&&0==this.car.a&&(this.car.v=Math.min(0,this.car.v+.01)),this.car.a>0&&this.car.v>=0?this.car.v=Math.min(this.maxSpeed,this.car.v+.005*this.car.a):this.car.a>0&&this.car.v<0&&(this.car.v=Math.min(this.maxSpeed,this.car.v+.03*this.car.a)),this.car.a<0&&this.car.v<=0?this.car.v=Math.max(-this.maxSpeed,this.car.v+.005*this.car.a):this.car.a<0&&this.car.v>0&&(this.car.v=Math.max(-this.maxSpeed,this.car.v+.03*this.car.a)),0==this.car.yaw_rate)this.car.x+=this.car.v*Math.cos(this.car.rotation)*t,this.car.y+=this.car.v*Math.sin(this.car.rotation)*t;else{var e=.01*this.car.yaw_rate*(this.car.v+.001)*Math.PI;this.car.x+=this.car.v/e*(Math.sin(this.car.rotation+e*t)-Math.sin(this.car.rotation)),this.car.y+=this.car.v/e*(Math.cos(this.car.rotation)-Math.cos(this.car.rotation+e*t)),this.car.rotation+=e*t}this.car.mx=Math.floor(this.car.x/d),this.car.my=Math.floor(this.car.y/d),this.car.checkAndsetNewRoad(),this.lidar.x=this.car.x,this.lidar.y=this.car.y,this.lidar.rotation=this.car.rotation;var i=this.detectInteractions(!0,!0),o=i.agentCollisions,n=i.onRoad;return o.length>0&&(this.car.v=0,this.car.vy=0),{agentCollisions:o,onRoad:n}},e}(M),j={env:k,editor:U,level:{fullCity:"embedded://level/fullCity",level1:"embedded://level/level1",level2:"embedded://level/level2",level3:"embedded://level/level3",level0:"embedded://level/level0"},motion:{BasicMotion:R,ControlMotion:B}};e.default=j}]).default; \ No newline at end of file diff --git a/src/asset_manager.ts b/src/asset_manager.ts index 8a3c97c..e7fa6f6 100644 --- a/src/asset_manager.ts +++ b/src/asset_manager.ts @@ -133,13 +133,16 @@ export class AssetManger { @line is Optional. 0 By default. */ if (line == undefined) - line = 0; + line = Math.min(1, Math.floor(Math.random() * 2)); let carById = this.level.findCarById(road.cars[0]); if (!force && road.cars.length >= 1 && carById && carById.core.line == line){ line = line == 0 ? 1:0; } car.line = line; + car.a = 0; + car.v = 0; + // Set the car on the road car.x = road.x; car.y = road.y; @@ -158,7 +161,6 @@ export class AssetManger { let line_side_factor_t = line == 0 ? 0:Math.PI; car.x += line_side_factor*Math.round(x_m); car.y += line_side_factor*Math.round(y_m); - // (x, y position) Relatif to the map car.mx = Math.floor(car.x / ROADSIZE); car.my = Math.floor(car.y / ROADSIZE); diff --git a/src/basic_motion_engine.ts b/src/basic_motion_engine.ts index 3f148fd..ace5827 100644 --- a/src/basic_motion_engine.ts +++ b/src/basic_motion_engine.ts @@ -21,6 +21,7 @@ export class BasicMotionEngine extends MotionEngine { */ private rotationStep: number; private actions: string[]; + private maxSpeed: number = 1.0; constructor(level: Level, options: BasicMotionOptions) { super(level); @@ -49,6 +50,8 @@ export class BasicMotionEngine extends MotionEngine { this.setUpKeyboard(); // Setup up velocity to 0 this.car.v = 0; + this.car.a = 0; // Acceleration + this.car.yaw_rate = 0; this.detectInteractions(); } diff --git a/src/bot_motion_engine.ts b/src/bot_motion_engine.ts index af35ba4..1dfc9af 100644 --- a/src/bot_motion_engine.ts +++ b/src/bot_motion_engine.ts @@ -182,6 +182,7 @@ export class BotMotionEngine extends MotionEngine { let turn = false; let nx = this.car.mx + is_next_pos.mx; let ny = this.car.my + is_next_pos.my; + if (!this.isRoad(nx, ny)) { turn = true; this.car.optionalTurn = false; diff --git a/src/car.ts b/src/car.ts index 23aae93..5428fab 100644 --- a/src/car.ts +++ b/src/car.ts @@ -11,6 +11,7 @@ import { CAR_IMG, Sprite, MAP, ROADSIZE, Container, Graphics } from "./global"; import { RoadSprite } from "./asset_manager"; +import { runInThisContext } from "vm"; var Global_carId = 0; @@ -55,6 +56,12 @@ export interface CarSprite extends PIXI.Sprite { optionalTurn?: boolean; agent?: boolean; v?: number; + a?: number; + yaw_rate?: number; + last_a?: number; + last_yaw_rate?: number; + next_road?: any; + turnedRandom?: any; } export interface LidarChild extends PIXI.Graphics { @@ -62,6 +69,14 @@ export interface LidarChild extends PIXI.Graphics { pt?: boolean; } +export interface State { + lidar?: number[][]; + linear?: number[]; + v?: number; + a?: number; + steering?: number; +} + export class Car { public level: Level|Editor; @@ -70,7 +85,6 @@ export class Car { public motion: any; private info: CarInfo; - public turnedRandom: any; constructor(level: Level|Editor, info: CarInfo, textures: any, options:CarOptions={}) { /* @@ -162,7 +176,8 @@ export class Car { // TOdo: CHECK something is strange here this.core.haveTurned = false; this.core.optionalTurn = false; - this.turnedRandom = undefined; + this.core.turnedRandom = undefined; + this.core.next_road = false; if (n_road) n_road.cars.push(this.core.carId); } @@ -181,19 +196,26 @@ export class Car { } } - getState(linear:boolean = false): number[][]|number[]{ + getState(): State { /* Get the current state of the car The state is the current value of each point of the lidar. */ - if (!linear) - return this.motion.state.map(function(arr: any) { return arr.slice(); }); - else{ - let state: number[] = []; - this.motion.state.map((row: number[]) => { state = state.concat(row);}); - return state; - } + let nState: State = {}; + + let linear: number[] = []; + this.motion.state.map((row: number[]) => { linear = linear.concat(row);}); + linear.push(this.core.v); + + nState.linear = linear; + nState.lidar = this.motion.state.map(function(arr: any) { return arr.slice(); }); + + nState.v = this.core.v; + nState.a = this.core.last_a; + nState.steering = this.core.last_yaw_rate; + + return nState; } step(delta: number, action:number|number[]=null){ diff --git a/src/control_motion_engine.ts b/src/control_motion_engine.ts index 73f93cc..437269c 100644 --- a/src/control_motion_engine.ts +++ b/src/control_motion_engine.ts @@ -10,6 +10,7 @@ import * as U from "./utils"; export class ControlMotionEngine extends MotionEngine { private actions: (string|number)[]; + private maxSpeed: number = 2.0; constructor(level: Level) { /* @@ -133,6 +134,9 @@ export class ControlMotionEngine extends MotionEngine { Step into the environement @delta (Float) time since the last update */ + this.car.last_a = this.car.a; + this.car.last_yaw_rate = this.car.yaw_rate; + // The car lose speed over time if (this.car.v > 0 && this.car.a == 0) this.car.v = Math.max(0, this.car.v - 0.01); @@ -140,14 +144,14 @@ export class ControlMotionEngine extends MotionEngine { this.car.v = Math.min(0, this.car.v + 0.01); if (this.car.a > 0 && this.car.v >= 0) - this.car.v = Math.min(1.5, this.car.v + this.car.a*0.01); + this.car.v = Math.min(this.maxSpeed, this.car.v + this.car.a*0.005); else if (this.car.a > 0 && this.car.v < 0){ - this.car.v = Math.min(1.5, this.car.v + this.car.a*0.03); + this.car.v = Math.min(this.maxSpeed, this.car.v + this.car.a*0.03); } if (this.car.a < 0 && this.car.v <= 0) - this.car.v = Math.max(-1.5, this.car.v + this.car.a*0.01); + this.car.v = Math.max(-this.maxSpeed, this.car.v + this.car.a*0.005); else if (this.car.a < 0 && this.car.v > 0){ - this.car.v = Math.max(-1.5, this.car.v + this.car.a*0.03); + this.car.v = Math.max(-this.maxSpeed, this.car.v + this.car.a*0.03); } if (this.car.yaw_rate == 0){ this.car.x += this.car.v * Math.cos(this.car.rotation)*delta; diff --git a/src/embedded.ts b/src/embedded.ts index b315f19..a2ea369 100644 --- a/src/embedded.ts +++ b/src/embedded.ts @@ -2,6 +2,7 @@ import {fullCity} from "./embedded/level/full_city"; import {level1} from "./embedded/level/level_1"; import {level0} from "./embedded/level/level_0"; import {level2} from "./embedded/level/level_2"; +import {level3} from "./embedded/level/level_3"; /** * Object used to enumerate each @@ -17,6 +18,7 @@ export interface embeddedUrlI { fullCity: string; level1: string; level2: string; + level3: string; level0: string; }; @@ -24,6 +26,7 @@ export const embeddedUrl: embeddedUrlI = { fullCity: "embedded://level/fullCity", level1: "embedded://level/level1", level2: "embedded://level/level2", + level3: "embedded://level/level3", level0: "embedded://level/level0", } @@ -32,6 +35,7 @@ export const embeddedContent: any = { fullCity: fullCity, level1: level1, level0: level0, - level2: level2 + level2: level2, + level3: level3 } } \ No newline at end of file diff --git a/src/embedded/level/level_2.ts b/src/embedded/level/level_2.ts index 49a32f8..88a98ea 100644 --- a/src/embedded/level/level_2.ts +++ b/src/embedded/level/level_2.ts @@ -1,114 +1,319 @@ export const level2: any = { - "cars": [ + "cars": [], + "road": [ { - "mx": 7, - "my": 7, - "line": 0 + "x": 300, + "y": 360 }, { - "mx": 9, - "my": 6, - "line": 0 + "x": 300, + "y": 360 }, { - "mx": 8, - "my": 6, - "line": 1 + "x": 360, + "y": 360 }, { - "mx": 10, - "my": 6, - "line": 1 + "x": 420, + "y": 360 + }, + { + "x": 240, + "y": 360 + }, + { + "x": 180, + "y": 360 + }, + { + "x": 120, + "y": 360 + }, + { + "x": 480, + "y": 360 + }, + { + "x": 480, + "y": 360 + }, + { + "x": 120, + "y": 360 + }, + { + "x": 60, + "y": 360 + }, + { + "x": 300, + "y": 300 + }, + { + "x": 300, + "y": 240 + }, + { + "x": 480, + "y": 360 + }, + { + "x": 480, + "y": 360 + }, + { + "x": 540, + "y": 360 + }, + { + "x": 300, + "y": 180 + }, + { + "x": 360, + "y": 180 + }, + { + "x": 240, + "y": 180 + }, + { + "x": 240, + "y": 180 + }, + { + "x": 180, + "y": 180 + }, + { + "x": 420, + "y": 180 + }, + { + "x": 420, + "y": 120 + }, + { + "x": 180, + "y": 120 + }, + { + "x": 300, + "y": 60 + }, + { + "x": 420, + "y": 60 + }, + { + "x": 180, + "y": 60 + }, + { + "x": 240, + "y": 60 + }, + { + "x": 360, + "y": 60 + }, + { + "x": 480, + "y": 60 + }, + { + "x": 120, + "y": 60 + }, + { + "x": 540, + "y": 60 + }, + { + "x": 540, + "y": 120 + }, + { + "x": 540, + "y": 180 + }, + { + "x": 540, + "y": 240 + }, + { + "x": 540, + "y": 300 + }, + { + "x": 60, + "y": 60 + }, + { + "x": 60, + "y": 120 + }, + { + "x": 60, + "y": 180 + }, + { + "x": 60, + "y": 240 + }, + { + "x": 60, + "y": 300 + }, + { + "x": 60, + "y": 240 + }, + { + "x": 360, + "y": 180 + }, + { + "x": 480, + "y": 360 + } + ], + "asset": [ + { + "x": 162, + "y": 264 + }, + { + "x": 407, + "y": 264 + }, + { + "x": 259, + "y": 126 + }, + { + "x": 306, + "y": 33 + }, + { + "x": 491, + "y": 148 + }, + { + "x": 391, + "y": 431 + }, + { + "x": 608, + "y": 35 + }, + { + "x": 484, + "y": 224 + }, + { + "x": 248, + "y": 297 + }, + { + "x": 376, + "y": 336 + }, + { + "x": 78, + "y": 429 + }, + { + "x": 1, + "y": 205 + }, + { + "x": 3, + "y": 106 + }, + { + "x": -46, + "y": 290 + }, + { + "x": 622, + "y": 154 + }, + { + "x": 617, + "y": 280 } ], "house": [ { - "x": 68, - "y": 317 - } - ], - "house2": [ - { - "x": 624, - "y": 84 + "x": 162, + "y": 264 }, { - "x": -15, - "y": 144 + "x": 622, + "y": 154 } ], "house3": [ { - "x": 618, - "y": 231 + "x": 407, + "y": 264 }, { - "x": 13, - "y": 441 - } - ], - "bench": [ - { - "x": 318, - "y": 216 - }, - { - "x": 422, - "y": 36 + "x": -46, + "y": 290 } ], "tree": [ { - "x": 151, - "y": 144 + "x": 259, + "y": 126 }, { - "x": 229, - "y": 175 + "x": 491, + "y": 148 }, { - "x": 326, - "y": 132 + "x": 391, + "y": 431 }, { - "x": 413, - "y": 167 + "x": 608, + "y": 35 }, { - "x": 487, - "y": 157 + "x": 484, + "y": 224 }, { - "x": 445, - "y": 123 + "x": 248, + "y": 297 }, { - "x": 511, + "x": 78, "y": 429 }, { - "x": 584, - "y": 427 + "x": 1, + "y": 205 }, { - "x": 80, - "y": 391 + "x": 3, + "y": 106 }, { - "x": 219, - "y": 409 + "x": 617, + "y": 280 + } + ], + "bench": [ + { + "x": 306, + "y": 33 }, { - "x": 207, - "y": 326 - }, - { - "x": 156, - "y": 426 - }, - { - "x": 162, - "y": 13 + "x": 376, + "y": 336 } ], "map": [ @@ -129,11 +334,11 @@ export const level2: any = { 0, "↱", "↔", + "↧", "↔", "↔", "↔", - "↔", - "↔", + "↧", "↔", "↰", 0 @@ -142,9 +347,35 @@ export const level2: any = { 0, "↕", 0, + "↕", 0, 0, 0, + "↕", + 0, + "↕", + 0 + ], + [ + 0, + "↕", + 0, + "↳", + "↔", + "↧", + "↠", + "↲", + 0, + "↕", + 0 + ], + [ + 0, + "↟", + 0, + 0, + 0, + "↕", 0, 0, 0, @@ -157,7 +388,7 @@ export const level2: any = { 0, 0, 0, - 0, + "↕", 0, 0, 0, @@ -170,10 +401,10 @@ export const level2: any = { "↔", "↔", "↔", - "↧", - "↔", + "↥", "↔", "↔", + "↠", "↲", 0 ], @@ -183,43 +414,17 @@ export const level2: any = { 0, 0, 0, - "↕", 0, 0, 0, 0, - 0 - ], - [ - 0, - 0, - 0, - 0, - 0, - "↕", - 0, - "↱", - "↔", - "↔", - "↔" - ], - [ - 0, - 0, - 0, - 0, - 0, - "↕", - 0, - "↕", - 0, 0, 0 ] ], "agent": { - "mx": 2, - "my": 4, + "mx": 4, + "my": 3, "line": 0, "motion": { "type": "BasicMotionEngine", diff --git a/src/embedded/level/level_3.ts b/src/embedded/level/level_3.ts new file mode 100644 index 0000000..5b5447b --- /dev/null +++ b/src/embedded/level/level_3.ts @@ -0,0 +1,816 @@ +export const level3: any = { + "cars": [ + { + "mx": 1, + "my": 3, + "line": 0 + }, + { + "mx": 1, + "my": 8, + "line": 0 + }, + { + "mx": 4, + "my": 8, + "line": 0 + }, + { + "mx": 7, + "my": 7, + "line": 1 + }, + { + "mx": 9, + "my": 6, + "line": 0 + }, + { + "mx": 11, + "my": 4, + "line": 1 + }, + { + "mx": 9, + "my": 3, + "line": 1 + }, + { + "mx": 7, + "my": 2, + "line": 1 + }, + { + "mx": 5, + "my": 1, + "line": 1 + }, + { + "mx": 2, + "my": 1, + "line": 1 + }, + { + "mx": 6, + "my": 8, + "line": 0 + }, + { + "mx": 1, + "my": 6, + "line": 0 + }, + { + "mx": 5, + "my": 5, + "line": 1 + }, + { + "mx": 4, + "my": 3, + "line": 1 + }, + { + "mx": 3, + "my": 4, + "line": 0 + }, + { + "mx": 4, + "my": 6, + "line": 0 + } + ], + "road": [ + { + "x": 120, + "y": 60 + }, + { + "x": 180, + "y": 60 + }, + { + "x": 240, + "y": 60 + }, + { + "x": 60, + "y": 60 + }, + { + "x": 60, + "y": 120 + }, + { + "x": 60, + "y": 180 + }, + { + "x": 60, + "y": 240 + }, + { + "x": 60, + "y": 300 + }, + { + "x": 60, + "y": 360 + }, + { + "x": 60, + "y": 420 + }, + { + "x": 60, + "y": 480 + }, + { + "x": 120, + "y": 480 + }, + { + "x": 180, + "y": 480 + }, + { + "x": 240, + "y": 480 + }, + { + "x": 300, + "y": 60 + }, + { + "x": 300, + "y": 120 + }, + { + "x": 300, + "y": 180 + }, + { + "x": 300, + "y": 480 + }, + { + "x": 300, + "y": 420 + }, + { + "x": 300, + "y": 360 + }, + { + "x": 300, + "y": 180 + }, + { + "x": 360, + "y": 180 + }, + { + "x": 420, + "y": 180 + }, + { + "x": 420, + "y": 120 + }, + { + "x": 420, + "y": 60 + }, + { + "x": 240, + "y": 240 + }, + { + "x": 300, + "y": 60 + }, + { + "x": 360, + "y": 60 + }, + { + "x": 240, + "y": 180 + }, + { + "x": 300, + "y": 180 + }, + { + "x": 300, + "y": 240 + }, + { + "x": 300, + "y": 300 + }, + { + "x": 240, + "y": 300 + }, + { + "x": 180, + "y": 180 + }, + { + "x": 180, + "y": 240 + }, + { + "x": 180, + "y": 300 + }, + { + "x": 360, + "y": 60 + }, + { + "x": 420, + "y": 60 + }, + { + "x": 420, + "y": 60 + }, + { + "x": 420, + "y": 120 + }, + { + "x": 420, + "y": 180 + }, + { + "x": 420, + "y": 180 + }, + { + "x": 480, + "y": 180 + }, + { + "x": 540, + "y": 180 + }, + { + "x": 660, + "y": 180 + }, + { + "x": 600, + "y": 180 + }, + { + "x": 660, + "y": 240 + }, + { + "x": 660, + "y": 300 + }, + { + "x": 660, + "y": 360 + }, + { + "x": 600, + "y": 360 + }, + { + "x": 540, + "y": 360 + }, + { + "x": 480, + "y": 360 + }, + { + "x": 420, + "y": 360 + }, + { + "x": 300, + "y": 480 + }, + { + "x": 360, + "y": 480 + }, + { + "x": 360, + "y": 420 + }, + { + "x": 360, + "y": 360 + }, + { + "x": 420, + "y": 360 + }, + { + "x": 420, + "y": 420 + }, + { + "x": 420, + "y": 480 + }, + { + "x": 360, + "y": 480 + }, + { + "x": 180, + "y": 360 + }, + { + "x": 240, + "y": 360 + }, + { + "x": 300, + "y": 360 + }, + { + "x": 300, + "y": 300 + }, + { + "x": 300, + "y": 240 + }, + { + "x": 180, + "y": 180 + }, + { + "x": 240, + "y": 180 + }, + { + "x": 300, + "y": 180 + }, + { + "x": 180, + "y": 240 + }, + { + "x": 180, + "y": 300 + }, + { + "x": 240, + "y": 180 + }, + { + "x": 180, + "y": 60 + }, + { + "x": 60, + "y": 300 + }, + { + "x": 300, + "y": 480 + }, + { + "x": 240, + "y": 480 + }, + { + "x": 600, + "y": 180 + }, + { + "x": 540, + "y": 180 + }, + { + "x": 660, + "y": 300 + }, + { + "x": 480, + "y": 360 + } + ], + "asset": [ + { + "x": 515, + "y": 262 + }, + { + "x": 378, + "y": 254 + }, + { + "x": 510, + "y": 89 + }, + { + "x": 626, + "y": 86 + }, + { + "x": 518, + "y": 445 + }, + { + "x": 631, + "y": 436 + }, + { + "x": 711, + "y": 434 + }, + { + "x": 243, + "y": 295 + }, + { + "x": 243, + "y": 246 + }, + { + "x": 253, + "y": 153 + }, + { + "x": 16, + "y": 521 + }, + { + "x": 129, + "y": 214 + }, + { + "x": 121, + "y": 228 + }, + { + "x": 238, + "y": 425 + }, + { + "x": 456, + "y": 273 + }, + { + "x": 584, + "y": 531 + }, + { + "x": 376, + "y": 558 + }, + { + "x": 63, + "y": 539 + }, + { + "x": 91, + "y": 559 + }, + { + "x": 4, + "y": 515 + }, + { + "x": -3, + "y": 315 + }, + { + "x": -3, + "y": 183 + }, + { + "x": 368, + "y": 188 + }, + { + "x": 500, + "y": 18 + }, + { + "x": 610, + "y": 16 + }, + { + "x": 685, + "y": 13 + }, + { + "x": 273, + "y": 0 + }, + { + "x": 738, + "y": 359 + }, + { + "x": 742, + "y": 181 + }, + { + "x": 752, + "y": 12 + } + ], + "house3": [ + { + "x": 515, + "y": 262 + }, + { + "x": 626, + "y": 86 + } + ], + "house2": [ + { + "x": 378, + "y": 254 + }, + { + "x": 631, + "y": 436 + }, + { + "x": 711, + "y": 434 + }, + { + "x": 752, + "y": 12 + } + ], + "house": [ + { + "x": 510, + "y": 89 + }, + { + "x": 518, + "y": 445 + }, + { + "x": 742, + "y": 181 + } + ], + "tree": [ + { + "x": 243, + "y": 295 + }, + { + "x": 243, + "y": 246 + }, + { + "x": 121, + "y": 228 + }, + { + "x": 238, + "y": 425 + }, + { + "x": 456, + "y": 273 + }, + { + "x": 584, + "y": 531 + }, + { + "x": 376, + "y": 558 + }, + { + "x": 91, + "y": 559 + }, + { + "x": 4, + "y": 515 + }, + { + "x": -3, + "y": 315 + }, + { + "x": -3, + "y": 183 + }, + { + "x": 368, + "y": 188 + }, + { + "x": 500, + "y": 18 + }, + { + "x": 610, + "y": 16 + }, + { + "x": 685, + "y": 13 + }, + { + "x": 273, + "y": 0 + }, + { + "x": 738, + "y": 359 + } + ], + "bench": [ + { + "x": 253, + "y": 153 + } + ], + "map": [ + [ + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0 + ], + [ + 0, + "↱", + "↔", + "↠", + "↔", + "↔", + "↔", + "↰", + 0, + 0, + 0, + 0, + 0 + ], + [ + 0, + "↕", + 0, + 0, + 0, + 0, + 0, + "↕", + 0, + 0, + 0, + 0, + 0 + ], + [ + 0, + "↕", + 0, + "↱", + "↠", + "↰", + 0, + "↳", + "↔", + "↠", + "↔", + "↰", + 0 + ], + [ + 0, + "↕", + 0, + "↕", + 0, + "↕", + 0, + 0, + 0, + 0, + 0, + "↕", + 0 + ], + [ + 0, + "↟", + 0, + "↕", + 0, + "↕", + 0, + 0, + 0, + 0, + 0, + "↟", + 0 + ], + [ + 0, + "↕", + 0, + "↳", + "↔", + "↲", + 0, + "↱", + "↠", + "↔", + "↔", + "↲", + 0 + ], + [ + 0, + "↕", + 0, + 0, + 0, + 0, + 0, + "↕", + 0, + 0, + 0, + 0, + 0 + ], + [ + 0, + "↳", + "↔", + "↔", + "↔", + "↠", + "↔", + "↲", + 0, + 0, + 0, + 0, + 0 + ], + [ + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0 + ] + ], + "agent": { + "mx": 3, + "my": 8, + "line": 0, + "motion": { + "type": "BasicMotionEngine", + "options": { + "rotationStep": 0.5, + "actions": [ + "UP", + "LEFT", + "RIGHT", + "DOWN", + "WAIT" + ] + } + } + } +} \ No newline at end of file diff --git a/src/level.ts b/src/level.ts index d675f92..8daaaea 100644 --- a/src/level.ts +++ b/src/level.ts @@ -105,17 +105,12 @@ export class Level extends World { */ this.agent.reset(); for (var c = 0; c < this.cars.length; c++) { - //this.cars[c].reset(); + this.cars[c].reset(); } } setReward(agent_col: any, on_road: any, action: any){ - /* - TODO: Let's the reward define in the agent class - */ - let reward = -0.1; - if (action == 0 || this.agent.core.v == 1) - reward += 0.5; + let reward = 0 + Math.max(0., this.agent.core.v) / this.agent.motion.maxSpeed; if (agent_col.length > 0){ reward = -1; } @@ -133,12 +128,16 @@ export class Level extends World { } - step(delta: number, action:number|number[]=null){ + step(delta: number, action:number|number[]=null, auto: boolean = true){ /* Process one step into the environement @delta (Float) time since the last update @action: (Integer) The action to take (can be null if no action) */ + if (auto && !this.steping){ + return; + } + // Go through all cars to move each one for (var c = 0; c < this.cars.length; c++) { if (this.cars[c].lidar && !this.cars[c].core.agent) // If this car can move diff --git a/src/metacar.ts b/src/metacar.ts index 9628ff2..38480fe 100644 --- a/src/metacar.ts +++ b/src/metacar.ts @@ -8,7 +8,7 @@ import {UIEvent} from "./ui_event"; import * as U from "./utils"; import { BasicMotionEngine, BasicMotionOptions } from "./basic_motion_engine"; import { ControlMotionEngine } from "./control_motion_engine"; -import { LidarInfoI } from "./car"; +import { LidarInfoI, State } from "./car"; /** * @local Chooce whether to load a file from the computer. @@ -18,6 +18,15 @@ export interface eventLoadOptions { local: boolean; } +/** + * @cars Shuffle the position of the other cars (true default) + * @agent Shuffle the position of the agent (true default) + */ +export interface shuffleConfig{ + agent: boolean; + cars: boolean; +} + export class MetaCar { private level: Level; @@ -132,6 +141,14 @@ export class MetaCar { this.level.render(val); } + /** + * Choose wheter the environment should step automaticly + * @param val True or False + */ + public steping(val: boolean){ + this.level.setSteping(val); + } + /** * Usefull method to save/download a string as file. * @content The content of the file @@ -157,8 +174,8 @@ export class MetaCar { * The size of the state depends of the size of the Lidar. * @return The state as a 2D Array or 1D Array (linear:true) */ - public getState(linear:boolean = false): number[][]|number[]{ - return this.level.agent.getState(linear); + public getState(): State{ + return this.level.agent.getState(); } /** @@ -167,7 +184,7 @@ export class MetaCar { @return Reward value */ public step(action: number|number[]): number{ - return this.level.step(1, action); + return this.level.step(1, action, false); } /** @@ -181,22 +198,32 @@ export class MetaCar { } /** - * Set the agent on a new random road on the map. + * Shuffle the position of the agent and the others + * cars. */ - randomRoadPosition(): void{ + shuffle(config: shuffleConfig): void{ /* This position */ - let roads = this.level.getRoads(); - let keys = Object.keys(roads); - keys.sort(function() {return Math.random()-0.5;}); - for (let k in keys){ - let road = roads[keys[k]]; - if (road.cars.length == 0){ - road.setCarPosition(this.level.agent.core); - break; + config = config || {cars: true, agent: true}; + config.cars = config.cars != undefined ? config.cars:true; + config.agent = config.agent != undefined ? config.agent:true; + + if (config.agent) { + let roads = this.level.getRoads(); + let keys = Object.keys(roads); + keys.sort(function() {return Math.random()-0.5;}); + for (let k in keys){ + let road = roads[keys[k]]; + if (road.cars.length == 0){ + road.setCarPosition(this.level.agent.core); + break; + } } } + if (config.cars){ + this.level.shuffleCarsPositions(); + } } /** diff --git a/src/ui_event.ts b/src/ui_event.ts index 929db62..0b2b6cd 100644 --- a/src/ui_event.ts +++ b/src/ui_event.ts @@ -61,6 +61,7 @@ export class UIEvent { // Listen the event button.addEventListener("click", () => { this.level.render(false); + this.level.setSteping(false); if (fc) fc(); }); } diff --git a/src/world.ts b/src/world.ts index ac9481e..1bfe47d 100644 --- a/src/world.ts +++ b/src/world.ts @@ -21,6 +21,7 @@ export class World { protected loop: any; // Loop method called for each render protected canvasId: string; // Id of the target canvas protected cars: Car[] = []; + protected steping: boolean = true; constructor(levelContent: LevelInfo, canvasId: string) { /* @@ -124,9 +125,11 @@ export class World { render(val: boolean){ if (val){ this.app.ticker.start(); + this.steping = true; } else{ this.app.ticker.stop(); + this.steping = false; } } @@ -144,4 +147,28 @@ export class World { return this.app.renderer.plugins.interaction.mouse.global; } + /** + * Stop stepping in the environment automaticly + */ + public setSteping(val: boolean): void{ + this.steping = val; + } + + public shuffleCarsPositions(){ + for (let c = 0; c < this.cars.length; c++){ + + let roads = this.roads; + let keys = Object.keys(roads); + keys.sort(function() {return Math.random()-0.5;}); + for (let k in keys){ + let road = roads[keys[k]]; + if (road.cars.length == 0){ + road.setCarPosition(this.cars[c].core); + break; + } + } + + } + } + } \ No newline at end of file