{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-01-19T02:39:09.387985Z",
     "start_time": "2018-01-19T02:39:09.291235Z"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import os\n",
    "os.environ['CUDA_VISIBLE_DEVICES']=\"\"\n",
    "os.environ[\"PYTHONPATH\"]='.'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-01-19T02:39:09.542108Z",
     "start_time": "2018-01-19T02:39:09.390592Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    }
   ],
   "source": [
    "%pylab --no-import-all inline\n",
    "%reload_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-01-19T02:39:09.722311Z",
     "start_time": "2018-01-19T02:39:09.552596Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['ddpg/train.py',\n",
       " '--logdir',\n",
       " './outputs/logs_ddpg',\n",
       " '--num-threads',\n",
       " '1',\n",
       " '--reward-scale',\n",
       " '1',\n",
       " '--actor-layers',\n",
       " '64-64-64',\n",
       " '--actor-layer-norm',\n",
       " '--actor-parameters-noise',\n",
       " '--actor-lr',\n",
       " '0.001',\n",
       " '--actor-lr-end',\n",
       " '0.000001',\n",
       " '--dynamics-lr',\n",
       " '0.0001',\n",
       " '--dynamics-lr-end',\n",
       " '0.0000001',\n",
       " '--critic-layers',\n",
       " '64-32',\n",
       " '--critic-layer-norm',\n",
       " '--critic-lr',\n",
       " '0.002',\n",
       " '--critic-lr-end',\n",
       " '0.000001',\n",
       " '--initial-epsilon',\n",
       " '0.5',\n",
       " '--final-epsilon',\n",
       " '0.001',\n",
       " '--tau',\n",
       " '0.0001']"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "os.sys.argv=\"ddpg/train.py --logdir ./outputs/logs_ddpg \\\n",
    "--num-threads 1 \\\n",
    "--reward-scale 1 \\\n",
    "--actor-layers 64-64-64 --actor-layer-norm --actor-parameters-noise --actor-lr 0.001 --actor-lr-end 0.000001 \\\n",
    "--dynamics-lr 0.0001 --dynamics-lr-end 0.0000001 \\\n",
    "--critic-layers 64-32 --critic-layer-norm --critic-lr 0.002 --critic-lr-end 0.000001 \\\n",
    "--initial-epsilon 0.5 --final-epsilon 0.001 --tau 0.0001\".split(\" \")\n",
    "os.sys.argv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-01-19T02:39:09.829347Z",
     "start_time": "2018-01-19T02:39:09.724292Z"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from ddpg.train import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-01-19T02:39:18.905244Z",
     "start_time": "2018-01-19T02:39:09.834586Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[2018-01-19 10:39:09,999] Making new env: Pendulum-v0\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Base (\n",
      "  (feature_net): LinearNet (\n",
      "    (net): Sequential (\n",
      "      (linear_0): NoisyLinear (3 -> 64)\n",
      "      (layer_norm_0): LayerNorm (\n",
      "      )\n",
      "      (act_0): ReLU ()\n",
      "      (linear_1): NoisyLinear (64 -> 64)\n",
      "      (layer_norm_1): LayerNorm (\n",
      "      )\n",
      "      (act_1): ReLU ()\n",
      "      (linear_2): NoisyLinear (64 -> 64)\n",
      "      (layer_norm_2): LayerNorm (\n",
      "      )\n",
      "      (act_2): ReLU ()\n",
      "    )\n",
      "  )\n",
      ")\n",
      "ActorHead (\n",
      "  (base): Base (\n",
      "    (feature_net): LinearNet (\n",
      "      (net): Sequential (\n",
      "        (linear_0): NoisyLinear (3 -> 64)\n",
      "        (layer_norm_0): LayerNorm (\n",
      "        )\n",
      "        (act_0): ReLU ()\n",
      "        (linear_1): NoisyLinear (64 -> 64)\n",
      "        (layer_norm_1): LayerNorm (\n",
      "        )\n",
      "        (act_1): ReLU ()\n",
      "        (linear_2): NoisyLinear (64 -> 64)\n",
      "        (layer_norm_2): LayerNorm (\n",
      "        )\n",
      "        (act_2): ReLU ()\n",
      "      )\n",
      "    )\n",
      "  )\n",
      "  (policy_net): LinearNet (\n",
      "    (net): Sequential (\n",
      "      (linear_0): Linear (64 -> 1)\n",
      "      (act_0): ELU (alpha=1.0)\n",
      "    )\n",
      "  )\n",
      ")\n",
      "CriticHead (\n",
      "  (base): Base (\n",
      "    (feature_net): LinearNet (\n",
      "      (net): Sequential (\n",
      "        (linear_0): NoisyLinear (3 -> 64)\n",
      "        (layer_norm_0): LayerNorm (\n",
      "        )\n",
      "        (act_0): ReLU ()\n",
      "        (linear_1): NoisyLinear (64 -> 64)\n",
      "        (layer_norm_1): LayerNorm (\n",
      "        )\n",
      "        (act_1): ReLU ()\n",
      "        (linear_2): NoisyLinear (64 -> 64)\n",
      "        (layer_norm_2): LayerNorm (\n",
      "        )\n",
      "        (act_2): ReLU ()\n",
      "      )\n",
      "    )\n",
      "  )\n",
      "  (value_net): Linear (64 -> 1)\n",
      ")\n",
      "DynamicsHead (\n",
      "  (base): Base (\n",
      "    (feature_net): LinearNet (\n",
      "      (net): Sequential (\n",
      "        (linear_0): NoisyLinear (3 -> 64)\n",
      "        (layer_norm_0): LayerNorm (\n",
      "        )\n",
      "        (act_0): ReLU ()\n",
      "        (linear_1): NoisyLinear (64 -> 64)\n",
      "        (layer_norm_1): LayerNorm (\n",
      "        )\n",
      "        (act_1): ReLU ()\n",
      "        (linear_2): NoisyLinear (64 -> 64)\n",
      "        (layer_norm_2): LayerNorm (\n",
      "        )\n",
      "        (act_2): ReLU ()\n",
      "      )\n",
      "    )\n",
      "  )\n",
      "  (value_net): LinearNet (\n",
      "    (net): Sequential (\n",
      "      (linear_0): Linear (65 -> 64)\n",
      "      (layer_norm_0): LayerNorm (\n",
      "      )\n",
      "      (act_0): ReLU ()\n",
      "    )\n",
      "  )\n",
      "  (value_net2): Linear (64 -> 3)\n",
      ")\n"
     ]
    }
   ],
   "source": [
    "os.environ['OMP_NUM_THREADS'] = '1'\n",
    "torch.set_num_threads(1)\n",
    "args = parse_args()\n",
    "train(args,\n",
    "      create_model,\n",
    "      create_act_update_fns,\n",
    "      train_multi_thread,\n",
    "      train_single_thread,\n",
    "      play_single_thread)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "start_time": "2018-01-18T08:39:00.927Z"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "start_time": "2018-01-18T08:39:00.929Z"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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