diff --git a/demo/webapp/public/js/DDPG/ddpg_agent.js b/demo/webapp/public/js/DDPG/ddpg_agent.js
index c6a3e51..553575b 100644
--- a/demo/webapp/public/js/DDPG/ddpg_agent.js
+++ b/demo/webapp/public/js/DDPG/ddpg_agent.js
@@ -11,7 +11,7 @@ class DDPGAgent {
         // Default Config
         this.config = {
             "stateSize": 17,
-            "nbActions": 1,
+            "nbActions": 2,
             "layerNorm": false,
             "normalizeObservations": true,
             "seed": 0,
@@ -19,9 +19,9 @@ class DDPGAgent {
             "batchSize": 64,
             "actorLr": 0.0001,
             "criticLr": 0.001,
-            "memorySize": 15000,
+            "memorySize": 20000,
             "gamma": 0.99,
-            "noiseDecay": 0.95,
+            "noiseDecay": 0.99,
             "rewardScale": 1,
             "nbEpochs": 500,
             "nbEpochsCycle": 20,
@@ -60,7 +60,7 @@ class DDPGAgent {
         // Pick an action
         const tfActions = this.ddpg.predict(tf.tensor2d([state]));
         const actions = tfActions.buffer().values;
-        agent.env.step([1., actions[0]]);
+        agent.env.step([actions[0], actions[1]]);
         tfActions.dispose();
     }
 
@@ -94,7 +94,7 @@ class DDPGAgent {
 
         // Step in the environment with theses actions
         let mAcions = tfActions.buffer().values;
-        let mReward = this.env.step([1., mAcions[0]]);
+        let mReward = this.env.step([mAcions[0], mAcions[1]]);
         this.rewardsList.push(mReward);
         // Get the new observations
         let mState = this.env.getState().linear;
@@ -106,7 +106,7 @@ class DDPGAgent {
         }
 
         // Add the new tuple to the buffer
-        this.ddpg.memory.append(mPreviousStep, [mAcions[0]], mReward, mState, mDone);
+        this.ddpg.memory.append(mPreviousStep, [mAcions[0], mAcions[1]], mReward, mState, mDone);
 
         // Dispose tensor
         tfPreviousStep.dispose();
@@ -176,7 +176,7 @@ class DDPGAgent {
             }
             if (this.ddpg.memory.length == this.config.memorySize){
                 this.noisyActions = Math.max(0.1, this.noisyActions * this.config.noiseDecay);
-                this.ddpg.noise.desiredActionStddev = Math.min(0.5, this.config.noiseDecay * this.ddpg.noise.desiredActionStddev);
+                this.ddpg.noise.desiredActionStddev = Math.max(0.1, this.config.noiseDecay * this.ddpg.noise.desiredActionStddev);
                 let lossValuesCritic = [];
                 let lossValuesActor = [];
                 console.time("Training");                
diff --git a/demo/webapp/public/js/DDPG/index.js b/demo/webapp/public/js/DDPG/index.js
index 2810b85..6ceed76 100644
--- a/demo/webapp/public/js/DDPG/index.js
+++ b/demo/webapp/public/js/DDPG/index.js
@@ -22,7 +22,7 @@ env.loop(() => {
 
     displayState("realtime_viewer", state.lidar, 200, 200);
     let reward = env.getLastReward();
-    const qValue = agent.getQvalue(state.linear, [state.steering]);
+    const qValue = agent.getQvalue(state.linear, [state.a, state.steering]);
     displayScores("realtime_viewer", [qValue], reward, ["Q(a, s)"]);
 });
 
diff --git a/demo/webapp/public/js/DDPG/noise.js b/demo/webapp/public/js/DDPG/noise.js
index 9c0ebb2..08c8978 100644
--- a/demo/webapp/public/js/DDPG/noise.js
+++ b/demo/webapp/public/js/DDPG/noise.js
@@ -16,8 +16,8 @@ class AdaptiveParamNoiseSpec {
      */
     constructor(conf){
         conf = conf || {};
-        this.initialStddev = conf.initialStddev || 0.3;
-        this.desiredActionStddev = conf.desiredActionStddev || 0.3;
+        this.initialStddev = conf.initialStddev || 0.4;
+        this.desiredActionStddev = conf.desiredActionStddev || 0.4;
         this.adoptionCoefficient = conf.adoptionCoefficient || 1.01;
         this.currentStddev = this.initialStddev;
     }
diff --git a/src/level.ts b/src/level.ts
index 605640c..f7f12f2 100644
--- a/src/level.ts
+++ b/src/level.ts
@@ -110,8 +110,7 @@ export class Level extends World {
     }
 
     setReward(agent_col: any, on_road: any, action: any){
-        let reward = 0;
-        //let reward = -0.8 + this.agent.core.v / this.agent.motion.maxSpeed;
+        let reward = 0 + Math.max(0., this.agent.core.v) / this.agent.motion.maxSpeed;
         if (agent_col.length > 0){
             reward = -1;
         }