Playing Atari with Deep Reinforcement Learning (OpenAI Gym).ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "env: THEANO_FLAGS='device=cpu'\n"
     ]
    }
   ],
   "source": [
    "from __future__ import print_function \n",
    "experiment_setup_name = \"tutorial.gym.atari.spaceinvaders-v0.cnn\"\n",
    "\n",
    "\n",
    "#gym game title\n",
    "GAME_TITLE = 'SpaceInvaders-v0'\n",
    "\n",
    "#how many parallel game instances can your machine tolerate\n",
    "N_PARALLEL_GAMES = 5\n",
    "\n",
    "#how long is one replay session from a batch\n",
    "#since we have window-like memory (no recurrent layers), we can use relatively small session weights\n",
    "replay_seq_len = 10\n",
    "\n",
    "\n",
    "#theano device selection. GPU is, as always, in preference, but not required\n",
    "%env THEANO_FLAGS='device=cpu'\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# This tutorial is a showcase on how to use AgentNet for OpenAI Gym environments\n",
    "\n",
    "\n",
    "* Space Invaders game as an example\n",
    "* Training a simple lasagne neural network for Q_learning objective\n",
    " * This example can be easily modified to use convolutional networks and/or recurrent agent memory. \n",
    " \n",
    "* Training via simple experience replay (explained below)\n",
    "* Only using utility recurrent layers for simplicity of this example\n",
    " * but adding a few RNNs or GRUs shouldn's be a problem\n",
    "* the network is trained with a simple one-step Q-learning for simplicity\n",
    "\n",
    "\n",
    "## About OpenAI Gym\n",
    "\n",
    "* Its a recently published platform that basicly allows you to train agents in a wide variety of environments with near-identical interface.\n",
    "* This is twice as awesome since now we don't need to write a new wrapper for every game\n",
    "* Go check it out!\n",
    "  * Blog post - https://openai.com/blog/openai-gym-beta/\n",
    "  * Github - https://github.com/openai/gym\n",
    "  \n",
    "### Installing it\n",
    " * If nothing changed on their side, to run this, you bacically need to follow their install instructions - \n",
    " \n",
    "```\n",
    "git clone https://github.com/openai/gym.git\n",
    "cd gym\n",
    "pip install -e .[all]\n",
    "```\n",
    "\n",
    "## New to AgentNet and Lasagne?\n",
    "* This is pretty much the basic tutorial for AgentNet, so it's okay not to know it.\n",
    "* We only require surface level knowledge of theano and lasagne, so you can just learn them as you go.\n",
    "* Alternatively, you can find Lasagne tutorials here:\n",
    " * Official mnist example: http://lasagne.readthedocs.io/en/latest/user/tutorial.html\n",
    " * From scratch: https://github.com/ddtm/dl-course/tree/master/Seminar4\n",
    " * From theano: https://github.com/craffel/Lasagne-tutorial/blob/master/examples/tutorial.ipynb\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Experiment setup\n",
    "* Here we basically just load the game"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:gym.envs.registration:Making new env: SpaceInvaders-v0\n",
      "[2016-05-26 17:14:29,688] Making new env: SpaceInvaders-v0\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x104e06090>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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YTEb0nWiUQXHaCgt1zGZBV1eEvr7hbaS6bo9Hx2oV9PRE0wauH+66XS4Nu13D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\nQpEjSjwKRY4o8SgUOaLEo1DkyP8PYNJrgeMPC0sAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1085fd550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import gym\n",
    "atari = gym.make(GAME_TITLE)\n",
    "atari.reset()\n",
    "plt.imshow(atari.render('rgb_array'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Game Parameters\n",
    "* observation dimensions, actions, etc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['NOOP', 'FIRE', 'RIGHT', 'LEFT', 'RIGHTFIRE', 'LEFTFIRE']\n"
     ]
    }
   ],
   "source": [
    "n_actions = atari.action_space.n\n",
    "observation_shape = (None,)+atari.observation_space.shape\n",
    "action_names = atari.get_action_meanings()\n",
    "print(action_names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "del atari"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# agent setup step by step\n",
    "* An agent implementation may contain these parts:\n",
    " * Observation(s)\n",
    "   * InputLayers where observed game states (here - images) are sent at each tick \n",
    " * Memory layer(s)\n",
    "   * A dictionary that maps \"New memory layers\" to \"prev memory layers\"\n",
    " * Policy layer (e.g. Q-values or probabilities)\n",
    "   * in this case, a lasagne dense layer based on observation layer\n",
    " * Resolver - acton picker layer\n",
    "   * chooses what action to take given Q-values\n",
    "   * in this case, the resolver has epsilon-greedy policy\n",
    "   \n",
    "   \n",
    "We are going to build something of this shape:\n",
    "\n",
    "(one can assume that the 'time' goes from left to right, inputs are at the bottom and outputs go to the top)\n",
    "\n",
    "\n",
    "\n",
    "![window_dqn_scheme](http://s32.postimg.org/yy5q3wadx/window_dqn.png)\n",
    "  \n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Agent observations\n",
    "\n",
    "* Here you define where observations (game images) appear in the network"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import lasagne\n",
    "from lasagne.layers import InputLayer, DimshuffleLayer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#image observation at current tick goes here\n",
    "observation_layer = InputLayer(observation_shape,\n",
    "                               name=\"images input\")\n",
    "\n",
    "\n",
    "#reshape to [batch, color, x, y] to allow for convolutional layers to work correctly\n",
    "observation_reshape = DimshuffleLayer(observation_layer,(0,3,1,2))\n",
    "\n",
    "observation_reshape = lasagne.layers.Pool2DLayer(observation_reshape,(2,2),mode='average_inc_pad')\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "  \n",
    "### Agent memory states\n",
    " * Here you can define arbitrary transitions between \"previous state\" variables and their next states\n",
    " * The rules are\n",
    "   * previous states must be input layers\n",
    "   * next states must have same shape as previous ones\n",
    "   * otherwise it can be any lasagne network\n",
    "   * AgentNet.memory has several useful layers\n",
    "   \n",
    " * During training and evaluation, your states will be updated recurrently\n",
    "   * next state at t=1 is given as previous state to t=2\n",
    " \n",
    " * Finally, you have to define a dictionary mapping new state -> previous state\n",
    "\n",
    "\n",
    "### In this demo\n",
    "Since we have almost fully observable environment AND we want to keep baseline simple, we shall use no recurrent units.\n",
    "However, Atari game environments are known to have __flickering__ effect where some sprites are shown only on odd frames and others on even ones - that was used to optimize performance at the time.\n",
    "To compensate for this, we shall use the memory layer called __WindowAugmentation__ which basically maintains a K previous time steps of what it is fed with.\n",
    "\n",
    "One can try to use\n",
    " * GRU - `from agentnet.memory import GRUMemoryLayer`\n",
    " * RNN - `from agentnet.memory import RNNCell`\n",
    " * any custom lasagne layers that compute new memory states\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "\n",
    "#memory\n",
    "#using simple window-based memory that stores several states\n",
    "#the SpaceInvaders environment does not need any more as it is almost fully-observed\n",
    "from agentnet.memory import WindowAugmentation\n",
    "\n",
    "\n",
    "window_size = 3\n",
    "\n",
    "\n",
    "#prev state input\n",
    "prev_window = InputLayer((None,window_size)+tuple(observation_reshape.output_shape[1:]),\n",
    "                        name = \"previous window state\")\n",
    "\n",
    "\n",
    "#our window\n",
    "window = WindowAugmentation(observation_reshape,\n",
    "                            prev_window,\n",
    "                            name = \"new window state\")\n",
    "\n",
    "\n",
    "\n",
    "memory_dict = {window:prev_window}\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Neural network body\n",
    "Our strategy, again:\n",
    " * take pixel-wise maximum over the window\n",
    " * apply some layers\n",
    " * use output layer to predict Q-values(see next)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from lasagne.layers import DropoutLayer,DenseLayer, ExpressionLayer\n",
    "#you may use any other lasagne layers, including convolutions, batch_norms, maxout, etc\n",
    "\n",
    "#pixel-wise maximum over the temporal window (to avoid flickering)\n",
    "window_max = ExpressionLayer(window,\n",
    "                             lambda a: a.max(axis=1),\n",
    "                             output_shape = (None,)+window.output_shape[2:])\n",
    "\n",
    "\n",
    "\n",
    "#a simple lasagne network (try replacing with any other lasagne network and see what works best)    \n",
    "nn = lasagne.layers.Conv2DLayer(window_max,32,filter_size=8,stride=(4,4), name='cnn0')\n",
    "nn = lasagne.layers.Conv2DLayer(nn,64,filter_size=8,stride=(4,4), name='cnn1 ыть')\n",
    "\n",
    "#nn = DropoutLayer(nn,name = \"dropout\", p=0.05) #will get deterministic during evaluation\n",
    "nn = DenseLayer(nn,num_units=256,name='dense1')\n",
    "\n",
    "\n",
    "#WARNING! if your network is computing too slowly, try decreasing the amount of neurons\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Agent policy and action picking\n",
    "* Since we are training a deep Q-network, we need it to predict Q-values and take actions.\n",
    "* Hence we define a lasagne layer that is used for action output\n",
    "\n",
    "* To pick actions, we use an epsilon-greedy resolver\n",
    "  * Note that resolver outputs particular action IDs and not probabilities.\n",
    "  * These actions are than sent into the environment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#q_eval\n",
    "q_eval = DenseLayer(nn,\n",
    "                   num_units = n_actions,\n",
    "                   nonlinearity=lasagne.nonlinearities.linear,\n",
    "                   name=\"QEvaluator\")\n",
    "\n",
    "#resolver\n",
    "from agentnet.resolver import EpsilonGreedyResolver\n",
    "resolver = EpsilonGreedyResolver(q_eval,name=\"resolver\")\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Finally, agent\n",
    "We declare that this network is and MDP agent with such and such inputs, states and outputs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from agentnet.agent import Agent\n",
    "#all together\n",
    "agent = Agent(observation_layer,\n",
    "              memory_dict,\n",
    "              q_eval,resolver)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[cnn0.W,\n",
       " cnn0.b,\n",
       " cnn1 ыть.W,\n",
       " cnn1 ыть.b,\n",
       " dense1.W,\n",
       " dense1.b,\n",
       " QEvaluator.W,\n",
       " QEvaluator.b]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Since it's a single lasagne network, one can get it's weights, output, etc\n",
    "weights = lasagne.layers.get_all_params(resolver,trainable=True)\n",
    "weights"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Agent step function\n",
    "* computes action and next state given observation and prev state\n",
    "* written in a generic way to support any recurrences, windows, LTMs, etc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#compile theano graph for one step decision making\n",
    "applier_fun = agent.get_react_function()\n",
    "\n",
    "#a nice pythonic interface\n",
    "def step(observation, prev_memories = 'zeros',batch_size = N_PARALLEL_GAMES):\n",
    "    \"\"\" returns actions and new states given observation and prev state\n",
    "    Prev state in default setup should be [prev window,]\"\"\"\n",
    "    #default to zeros\n",
    "    if prev_memories == 'zeros':\n",
    "        prev_memories = [np.zeros((batch_size,)+tuple(mem.output_shape[1:]),\n",
    "                                  dtype='float32') \n",
    "                         for mem in agent.agent_states]\n",
    "    res = applier_fun(np.array(observation),*prev_memories)\n",
    "    action = res[0]\n",
    "    memories = res[1:]\n",
    "    return action,memories"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Create and manage a pool of atari sessions to play with\n",
    "\n",
    "* To make training more stable, we shall have an entire batch of game sessions each happening independent of others\n",
    "* We define a small container that stores\n",
    " * game emulators\n",
    " * last agent observations\n",
    " * agent memories at last time tick\n",
    "* This allows us to instantly continue a session from where it stopped\n",
    "\n",
    "\n",
    "\n",
    "* Why several parallel agents help training: http://arxiv.org/pdf/1602.01783v1.pdf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:gym.envs.registration:Making new env: SpaceInvaders-v0\n",
      "[2016-05-26 17:24:31,826] Making new env: SpaceInvaders-v0\n",
      "INFO:gym.envs.registration:Making new env: SpaceInvaders-v0\n",
      "[2016-05-26 17:24:31,851] Making new env: SpaceInvaders-v0\n",
      "INFO:gym.envs.registration:Making new env: SpaceInvaders-v0\n",
      "[2016-05-26 17:24:31,874] Making new env: SpaceInvaders-v0\n",
      "INFO:gym.envs.registration:Making new env: SpaceInvaders-v0\n",
      "[2016-05-26 17:24:31,910] Making new env: SpaceInvaders-v0\n",
      "INFO:gym.envs.registration:Making new env: SpaceInvaders-v0\n",
      "[2016-05-26 17:24:31,946] Making new env: SpaceInvaders-v0\n"
     ]
    }
   ],
   "source": [
    "from agentnet.experiments.openai_gym.pool import GamePool\n",
    "\n",
    "pool = GamePool(GAME_TITLE, N_PARALLEL_GAMES)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[['NOOP' 'LEFT' 'LEFT' 'LEFT' 'RIGHT']\n",
      " ['NOOP' 'LEFT' 'LEFT' 'LEFT' 'LEFT']\n",
      " ['NOOP' 'LEFT' 'LEFT' 'LEFT' 'LEFT']]\n",
      "CPU times: user 880 ms, sys: 177 ms, total: 1.06 s\n",
      "Wall time: 971 ms\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "observation_log,action_log,reward_log,_,_,_  = pool.interact(step,50)\n",
    "\n",
    "print(np.array(action_names)[np.array(action_log)[:3,:5]])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# experience replay pool\n",
    "\n",
    "Since our network exists in a theano graph and OpenAI gym doesn't, we shall train out network via experience replay.\n",
    "\n",
    "To do that in AgentNet, one can use a SessionPoolEnvironment.\n",
    "\n",
    "It's simple: you record new sessions using `interact(...)`, and than immediately train on them.\n",
    "\n",
    "1. Interact with Atari, get play sessions\n",
    "2. Store them into session environment\n",
    "3. Train on them\n",
    "4. Repeat\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#Create an environment with all default parameters\n",
    "from agentnet.environment import SessionPoolEnvironment\n",
    "env = SessionPoolEnvironment(observations = observation_layer,\n",
    "                             actions=resolver,\n",
    "                             agent_memories=agent.agent_states)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def update_pool(env, pool,n_steps=100):\n",
    "    \"\"\" a function that creates new sessions and ads them into the pool\n",
    "    throwing the old ones away entirely for simplicity\"\"\"\n",
    "\n",
    "    preceding_memory_states = list(pool.prev_memory_states)\n",
    "    \n",
    "    #get interaction sessions\n",
    "    observation_tensor,action_tensor,reward_tensor,_,is_alive_tensor,_= pool.interact(step,n_steps=n_steps)\n",
    "        \n",
    "    #load them into experience replay environment\n",
    "    env.load_sessions(observation_tensor,action_tensor,reward_tensor,is_alive_tensor,preceding_memory_states)\n",
    "    \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#load first  sessions\n",
    "update_pool(env,pool,replay_seq_len)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A more sophisticated way of training is to store a large pool of sessions and train on random batches of them. \n",
    "* Why that is expected to be better - http://www.nature.com/nature/journal/v518/n7540/full/nature14236.html\n",
    "* Or less proprietary - https://www.cs.toronto.edu/~vmnih/docs/dqn.pdf\n",
    "\n",
    "To do that, one might make use of\n",
    "* ```env.load_sessions(...)``` - load new sessions\n",
    "* ```env.get_session_updates(...)``` - does the same thing via theano updates (advanced)\n",
    "* ```batch_env = env.sample_session_batch(batch_size, ...)``` - create an experience replay environment that contains batch_size random sessions from env (rerolled each time). Should be used in training instead of env.\n",
    "* ```env.select_session_batch(indices)``` does the same thing deterministically.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Interacting with environment\n",
    "* An agent has a method that produces symbolic environment interaction sessions\n",
    "* Such sessions are in sequences of observations, agent memory, actions, q-values,etc\n",
    "  * one has to pre-define maximum session length.\n",
    "\n",
    "* SessionPool also stores rewards (Q-learning objective)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Training via experience replay\n",
    "\n",
    "* We use agent we have created to replay environment interactions inside Theano\n",
    "* to than train on the replayed sessions via theano gradient propagation\n",
    "* this is essentially basic Lasagne code after the following cell"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#get agent's Qvalues obtained via experience replay\n",
    "_,_,_,_,qvalues_seq = agent.get_sessions(\n",
    "    env,\n",
    "    session_length=replay_seq_len,\n",
    "    batch_size=env.batch_size,\n",
    "    optimize_experience_replay=True,\n",
    ")\n",
    "\n",
    "\n",
    "#The \"_\"s are\n",
    "#first - environment states - which is empty since we are using session pool as our environment\n",
    "#secund - observation sequences - whatever agent recieved at observation input(s) on each tick\n",
    "#third - a dictionary of all agent memory units (RNN, GRU, NTM) - empty as we use none of them\n",
    "#last - \"imagined\" actions - actions agent would pick now if he was in that situation \n",
    "#                              - irrelevant since we are replaying and not actually playing the game now\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Evaluating loss function\n",
    "* In this part we are using some basic Reinforcement Learning methods (here - Q-learning) to train\n",
    "* AgentNet has plenty of such methods, but we shall use the simple Q_learning for now.\n",
    "* Later you can try:\n",
    " * SARSA - simpler on-policy algorithms\n",
    " * N-step q-learning (requires n_steps parameter)\n",
    " * Advantage Actor-Critic (requires state values and probabilities instead of Q-values)\n",
    "\n",
    "\n",
    "* The basic interface is .get_elementwise_objective \n",
    "  * it returns loss function (here - squared error against reference Q-values) values at each batch and tick\n",
    "  \n",
    "* If you want to do it the hard way instead, try .get_reference_Qvalues and compute errors on ya own\n",
    "  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#get reference Qvalues according to Qlearning algorithm\n",
    "\n",
    "\n",
    "from agentnet.learning import qlearning\n",
    "\n",
    "#gamma - delayed reward coefficient - what fraction of reward is retained if it is obtained one tick later\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "#IMPORTANT!\n",
    "# If you are training on a game that has rewards far outside some [-5,+5]\n",
    "# it is a good idea to downscale them to avoid divergence\n",
    "scaled_reward_seq = env.rewards\n",
    "#For SpaceInvaders, however, not scaling rewards is at least working\n",
    "\n",
    "\n",
    "elwise_mse_loss = qlearning.get_elementwise_objective(qvalues_seq,\n",
    "                                                        env.actions[0],\n",
    "                                                        scaled_reward_seq,\n",
    "                                                        env.is_alive,\n",
    "                                                        gamma_or_gammas=0.99,)\n",
    "\n",
    "#compute mean over \"alive\" fragments\n",
    "mse_loss = elwise_mse_loss.sum() / env.is_alive.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#regularize network weights\n",
    "\n",
    "from lasagne.regularization import regularize_network_params, l2\n",
    "reg_l2 = regularize_network_params(resolver,l2)*10**-5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "loss = mse_loss + reg_l2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Compute weight updates\n",
    "updates = lasagne.updates.adadelta(loss,weights,learning_rate=0.01)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#mean session reward\n",
    "mean_session_reward = env.rewards.sum(axis=1).mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Compile train and evaluation functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import theano\n",
    "train_fun = theano.function([],[loss,mean_session_reward],updates=updates)\n",
    "evaluation_fun = theano.function([],[loss,mse_loss,reg_l2,mean_session_reward])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Session visualization tools\n",
    "\n",
    "Just a helper function that draws current game images."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def display_sessions(max_n_sessions = 3):\n",
    "    \"\"\"just draw random images\"\"\"\n",
    "    \n",
    "    plt.figure(figsize=[15,8])\n",
    "    \n",
    "    pictures = [atari.render(\"rgb_array\") for atari in pool.games[:max_n_sessions]]\n",
    "    for i,pic in enumerate(pictures):\n",
    "        plt.subplot(1,max_n_sessions,i+1)\n",
    "        plt.imshow(pic)\n",
    "    plt.show()\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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gwQN7zhOykSibKkPZRKpFDbWHGLOm6fX1udDTIyEa1VAsGohGNRiG1TBpa7OG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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x116c18a10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#visualize untrained network performance (which is mostly random)\n",
    "display_sessions()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Training loop"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import os"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from agentnet.display import Metrics\n",
    "score_log = Metrics()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#starting epoch\n",
    "epoch_counter = 1\n",
    "#moving average estimation\n",
    "alpha = 0.1\n",
    "ma_reward_current = 0.\n",
    "ma_reward_greedy = 0."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch 5,loss 25.45360, epsilon 0.49776, rewards: ( e-greedy 0.00000, greedy 0.00000) \n",
      "rec 25.449 reg 0.005\n",
      "epoch 10,loss 20.92396, epsilon 0.49552, rewards: ( e-greedy 0.60000, greedy 0.80000) \n",
      "rec 20.919 reg 0.005\n",
      "epoch 15,loss 21.53044, epsilon 0.49330, rewards: ( e-greedy 1.54000, greedy 0.72000) \n",
      "rec 21.526 reg 0.005\n",
      "epoch 20,loss 4.22164, epsilon 0.49109, rewards: ( e-greedy 1.38600, greedy 0.64800) \n",
      "rec 4.217 reg 0.005\n",
      "epoch 25,loss 11.51695, epsilon 0.48889, rewards: ( e-greedy 1.24740, greedy 0.58320) \n",
      "rec 11.512 reg 0.005\n",
      "epoch 30,loss 8.60155, epsilon 0.48670, rewards: ( e-greedy 1.12266, greedy 0.52488) \n",
      "rec 8.597 reg 0.005\n",
      "epoch 35,loss 9.17407, epsilon 0.48452, rewards: ( e-greedy 1.01039, greedy 0.47239) \n",
      "rec 9.169 reg 0.005\n",
      "epoch 40,loss 9.87727, epsilon 0.48236, rewards: ( e-greedy 0.90935, greedy 0.42515) \n",
      "rec 9.873 reg 0.005\n",
      "epoch 45,loss 10.18141, epsilon 0.48020, rewards: ( e-greedy 0.81842, greedy 0.38264) \n",
      "rec 10.177 reg 0.005\n",
      "epoch 50,loss 21.08929, epsilon 0.47805, rewards: ( e-greedy 1.03658, greedy 0.34437) \n",
      "rec 21.085 reg 0.005\n",
      "epoch 55,loss 16.01702, epsilon 0.47592, rewards: ( e-greedy 1.23292, greedy 0.70994) \n",
      "rec 16.012 reg 0.005\n",
      "epoch 60,loss 13.88508, epsilon 0.47379, rewards: ( e-greedy 1.10963, greedy 0.93894) \n",
      "rec 13.880 reg 0.005\n",
      "epoch 65,loss 15.65966, epsilon 0.47168, rewards: ( e-greedy 0.99866, greedy 1.04505) \n",
      "rec 15.655 reg 0.005\n",
      "epoch 70,loss 21.20852, epsilon 0.46958, rewards: ( e-greedy 1.19880, greedy 1.14054) \n",
      "rec 21.204 reg 0.005\n",
      "epoch 75,loss 24.05781, epsilon 0.46748, rewards: ( e-greedy 1.27892, greedy 1.22649) \n",
      "rec 24.053 reg 0.005\n",
      "epoch 80,loss 14.93107, epsilon 0.46540, rewards: ( e-greedy 1.35103, greedy 5.10384) \n",
      "rec 14.926 reg 0.005\n",
      "epoch 85,loss 11.84498, epsilon 0.46333, rewards: ( e-greedy 1.21592, greedy 5.29346) \n",
      "rec 11.840 reg 0.005\n",
      "epoch 90,loss 18.23669, epsilon 0.46127, rewards: ( e-greedy 1.09433, greedy 5.26411) \n",
      "rec 18.232 reg 0.005\n",
      "epoch 95,loss 13.18461, epsilon 0.45922, rewards: ( e-greedy 1.08490, greedy 5.33770) \n",
      "rec 13.180 reg 0.005\n",
      "epoch 100,loss 12.26003, epsilon 0.45718, rewards: ( e-greedy 0.97641, greedy 4.80393) \n",
      "rec 12.255 reg 0.005\n",
      "epoch 105,loss 24.26037, epsilon 0.45515, rewards: ( e-greedy 1.37877, greedy 4.32354) \n",
      "rec 24.256 reg 0.005\n",
      "epoch 110,loss 44.59273, epsilon 0.45313, rewards: ( e-greedy 2.24089, greedy 3.89118) \n",
      "rec 44.588 reg 0.005\n",
      "epoch 115,loss 16.29836, epsilon 0.45111, rewards: ( e-greedy 2.01680, greedy 4.40206) \n",
      "rec 16.294 reg 0.005\n",
      "epoch 120,loss 20.26903, epsilon 0.44911, rewards: ( e-greedy 2.11512, greedy 4.26186) \n",
      "rec 20.264 reg 0.005\n",
      "epoch 125,loss 46.72947, epsilon 0.44712, rewards: ( e-greedy 2.80361, greedy 4.43567) \n",
      "rec 46.725 reg 0.005\n",
      "epoch 130,loss 12.51770, epsilon 0.44514, rewards: ( e-greedy 2.62325, greedy 4.49211) \n",
      "rec 12.513 reg 0.005\n",
      "epoch 135,loss 14.60101, epsilon 0.44317, rewards: ( e-greedy 2.36092, greedy 4.04289) \n",
      "rec 14.596 reg 0.005\n",
      "epoch 140,loss 31.77839, epsilon 0.44121, rewards: ( e-greedy 3.02483, greedy 4.03861) \n",
      "rec 31.774 reg 0.005\n",
      "epoch 145,loss 13.93904, epsilon 0.43926, rewards: ( e-greedy 2.72235, greedy 3.73474) \n",
      "rec 13.934 reg 0.005\n",
      "epoch 150,loss 15.34534, epsilon 0.43732, rewards: ( e-greedy 2.85011, greedy 3.36127) \n",
      "rec 15.341 reg 0.005\n",
      "epoch 155,loss 12.45872, epsilon 0.43539, rewards: ( e-greedy 2.66510, greedy 3.62514) \n",
      "rec 12.454 reg 0.005\n",
      "epoch 160,loss 9.78221, epsilon 0.43346, rewards: ( e-greedy 2.39859, greedy 3.56263) \n",
      "rec 9.778 reg 0.005\n",
      "epoch 165,loss 8.37318, epsilon 0.43155, rewards: ( e-greedy 2.15873, greedy 3.30637) \n",
      "rec 8.369 reg 0.005\n",
      "epoch 170,loss 5.51971, epsilon 0.42965, rewards: ( e-greedy 1.94286, greedy 2.97573) \n",
      "rec 5.515 reg 0.005\n",
      "epoch 175,loss 10.89277, epsilon 0.42776, rewards: ( e-greedy 1.74857, greedy 2.67816) \n",
      "rec 10.888 reg 0.005\n",
      "epoch 180,loss 13.60367, epsilon 0.42587, rewards: ( e-greedy 2.07372, greedy 2.91034) \n",
      "rec 13.599 reg 0.005\n",
      "epoch 185,loss 11.93004, epsilon 0.42400, rewards: ( e-greedy 1.86634, greedy 2.61931) \n",
      "rec 11.925 reg 0.005\n",
      "epoch 190,loss 27.69475, epsilon 0.42213, rewards: ( e-greedy 2.17971, greedy 2.35738) \n",
      "rec 27.690 reg 0.005\n",
      "epoch 195,loss 9.86823, epsilon 0.42028, rewards: ( e-greedy 1.96174, greedy 2.12164) \n",
      "rec 9.864 reg 0.005\n",
      "epoch 200,loss 8.13705, epsilon 0.41843, rewards: ( e-greedy 1.76557, greedy 1.90947) \n",
      "rec 8.132 reg 0.005\n",
      "epoch 205,loss 17.01845, epsilon 0.41659, rewards: ( e-greedy 2.08901, greedy 1.81853) \n",
      "rec 17.014 reg 0.005\n",
      "epoch 210,loss 27.32363, epsilon 0.41476, rewards: ( e-greedy 2.48011, greedy 1.63667) \n",
      "rec 27.319 reg 0.005\n",
      "epoch 215,loss 809.93102, epsilon 0.41294, rewards: ( e-greedy 6.63210, greedy 1.47301) \n",
      "rec 809.926 reg 0.005\n",
      "epoch 220,loss 13.57924, epsilon 0.41113, rewards: ( e-greedy 6.26889, greedy 2.12571) \n",
      "rec 13.575 reg 0.005\n",
      "epoch 225,loss 7.32845, epsilon 0.40933, rewards: ( e-greedy 5.84200, greedy 2.21314) \n",
      "rec 7.324 reg 0.005\n",
      "epoch 230,loss 7.61049, epsilon 0.40754, rewards: ( e-greedy 5.25780, greedy 1.99182) \n",
      "rec 7.606 reg 0.005\n",
      "epoch 235,loss 6.84727, epsilon 0.40576, rewards: ( e-greedy 4.83202, greedy 1.79264) \n",
      "rec 6.843 reg 0.005\n",
      "epoch 240,loss 34.05070, epsilon 0.40398, rewards: ( e-greedy 5.54882, greedy 1.71338) \n",
      "rec 34.046 reg 0.005\n",
      "epoch 245,loss 6.88506, epsilon 0.40222, rewards: ( e-greedy 4.99394, greedy 1.54204) \n",
      "rec 6.880 reg 0.005\n",
      "epoch 250,loss 6.62391, epsilon 0.40046, rewards: ( e-greedy 4.49454, greedy 1.88783) \n",
      "rec 6.619 reg 0.005\n",
      "epoch 255,loss 7.12217, epsilon 0.39871, rewards: ( e-greedy 4.04509, greedy 1.69905) \n",
      "rec 7.118 reg 0.005\n",
      "epoch 260,loss 9.60314, epsilon 0.39697, rewards: ( e-greedy 3.74058, greedy 3.22915) \n",
      "rec 9.599 reg 0.005\n",
      "epoch 265,loss 15.46298, epsilon 0.39524, rewards: ( e-greedy 3.76652, greedy 2.90623) \n",
      "rec 15.458 reg 0.005\n",
      "epoch 270,loss 9.85598, epsilon 0.39352, rewards: ( e-greedy 3.38987, greedy 2.71561) \n",
      "rec 9.851 reg 0.005\n",
      "epoch 275,loss 13.52420, epsilon 0.39181, rewards: ( e-greedy 3.25088, greedy 3.54405) \n",
      "rec 13.520 reg 0.005\n",
      "epoch 280,loss 7.88220, epsilon 0.39010, rewards: ( e-greedy 2.92579, greedy 3.18964) \n",
      "rec 7.878 reg 0.005\n",
      "epoch 285,loss 15.37297, epsilon 0.38841, rewards: ( e-greedy 3.03321, greedy 3.47068) \n",
      "rec 15.368 reg 0.005\n",
      "epoch 290,loss 14.51700, epsilon 0.38672, rewards: ( e-greedy 3.32989, greedy 3.12361) \n",
      "rec 14.512 reg 0.005\n",
      "epoch 295,loss 5.47843, epsilon 0.38504, rewards: ( e-greedy 3.19690, greedy 2.81125) \n",
      "rec 5.474 reg 0.005\n",
      "epoch 300,loss 9.12472, epsilon 0.38337, rewards: ( e-greedy 3.07721, greedy 2.53012) \n",
      "rec 9.120 reg 0.005\n",
      "epoch 305,loss 7.74769, epsilon 0.38171, rewards: ( e-greedy 2.76949, greedy 2.67711) \n",
      "rec 7.743 reg 0.005\n",
      "epoch 310,loss 9.92043, epsilon 0.38005, rewards: ( e-greedy 2.59254, greedy 2.80940) \n",
      "rec 9.916 reg 0.005\n",
      "epoch 315,loss 6.10759, epsilon 0.37840, rewards: ( e-greedy 2.33329, greedy 2.92846) \n",
      "rec 6.103 reg 0.005\n",
      "epoch 320,loss 7.35461, epsilon 0.37677, rewards: ( e-greedy 2.09996, greedy 2.63561) \n",
      "rec 7.350 reg 0.005\n",
      "epoch 325,loss 4.71977, epsilon 0.37514, rewards: ( e-greedy 1.88996, greedy 2.77205) \n",
      "rec 4.715 reg 0.005\n",
      "epoch 330,loss 17.74359, epsilon 0.37352, rewards: ( e-greedy 2.20097, greedy 2.49485) \n",
      "rec 17.739 reg 0.005\n",
      "epoch 335,loss 18.83461, epsilon 0.37190, rewards: ( e-greedy 2.48087, greedy 2.64536) \n",
      "rec 18.830 reg 0.005\n",
      "epoch 340,loss 10.44195, epsilon 0.37030, rewards: ( e-greedy 2.23278, greedy 2.78083) \n",
      "rec 10.437 reg 0.005\n",
      "epoch 345,loss 11.40063, epsilon 0.36870, rewards: ( e-greedy 2.00951, greedy 3.40274) \n",
      "rec 11.396 reg 0.005\n",
      "epoch 350,loss 24.12577, epsilon 0.36711, rewards: ( e-greedy 2.40855, greedy 3.56247) \n",
      "rec 24.121 reg 0.005\n",
      "epoch 355,loss 10.55050, epsilon 0.36553, rewards: ( e-greedy 2.26770, greedy 3.50622) \n",
      "rec 10.546 reg 0.005\n",
      "epoch 360,loss 9.46017, epsilon 0.36395, rewards: ( e-greedy 2.04093, greedy 3.55560) \n",
      "rec 9.456 reg 0.005\n",
      "epoch 365,loss 16.12292, epsilon 0.36239, rewards: ( e-greedy 2.43684, greedy 3.70004) \n",
      "rec 16.118 reg 0.005\n",
      "epoch 370,loss 13.79445, epsilon 0.36083, rewards: ( e-greedy 2.49315, greedy 3.83004) \n",
      "rec 13.790 reg 0.005\n",
      "epoch 375,loss 11.30020, epsilon 0.35928, rewards: ( e-greedy 2.24384, greedy 4.14703) \n",
      "rec 11.296 reg 0.005\n",
      "epoch 380,loss 29.38869, epsilon 0.35774, rewards: ( e-greedy 2.71945, greedy 4.33233) \n",
      "rec 29.384 reg 0.005\n",
      "epoch 385,loss 6.54958, epsilon 0.35620, rewards: ( e-greedy 2.44751, greedy 3.89910) \n",
      "rec 6.545 reg 0.005\n",
      "epoch 390,loss 6.77418, epsilon 0.35468, rewards: ( e-greedy 2.20276, greedy 3.50919) \n",
      "rec 6.770 reg 0.005\n",
      "epoch 395,loss 7.66011, epsilon 0.35316, rewards: ( e-greedy 1.98248, greedy 3.15827) \n",
      "rec 7.655 reg 0.005\n",
      "epoch 400,loss 4.90709, epsilon 0.35164, rewards: ( e-greedy 1.78423, greedy 3.04244) \n",
      "rec 4.902 reg 0.005\n",
      "epoch 405,loss 3.60206, epsilon 0.35014, rewards: ( e-greedy 1.60581, greedy 2.73820) \n",
      "rec 3.597 reg 0.005\n",
      "epoch 410,loss 6.40989, epsilon 0.34864, rewards: ( e-greedy 1.64523, greedy 2.46438) \n",
      "rec 6.405 reg 0.005\n",
      "epoch 415,loss 5.97009, epsilon 0.34715, rewards: ( e-greedy 1.68071, greedy 2.51794) \n",
      "rec 5.965 reg 0.005\n",
      "epoch 420,loss 5.15802, epsilon 0.34567, rewards: ( e-greedy 1.51264, greedy 2.26615) \n",
      "rec 5.153 reg 0.005\n",
      "epoch 425,loss 5.28103, epsilon 0.34420, rewards: ( e-greedy 1.36137, greedy 2.03953) \n",
      "rec 5.276 reg 0.005\n",
      "epoch 430,loss 5.48613, epsilon 0.34273, rewards: ( e-greedy 1.42523, greedy 1.83558) \n",
      "rec 5.481 reg 0.005\n",
      "epoch 435,loss 5.73569, epsilon 0.34127, rewards: ( e-greedy 1.28271, greedy 2.25202) \n",
      "rec 5.731 reg 0.005\n",
      "epoch 440,loss 3.86507, epsilon 0.33982, rewards: ( e-greedy 1.15444, greedy 3.02682) \n",
      "rec 3.860 reg 0.005\n",
      "epoch 445,loss 7.93134, epsilon 0.33837, rewards: ( e-greedy 1.33900, greedy 2.72414) \n",
      "rec 7.927 reg 0.005\n",
      "epoch 450,loss 5.12825, epsilon 0.33693, rewards: ( e-greedy 1.20510, greedy 2.95172) \n",
      "rec 5.124 reg 0.005\n",
      "epoch 455,loss 3.78076, epsilon 0.33550, rewards: ( e-greedy 1.08459, greedy 3.05655) \n",
      "rec 3.776 reg 0.005\n",
      "epoch 460,loss 4.31459, epsilon 0.33408, rewards: ( e-greedy 0.97613, greedy 3.15090) \n",
      "rec 4.310 reg 0.005\n",
      "epoch 465,loss 5.21092, epsilon 0.33266, rewards: ( e-greedy 0.87852, greedy 2.83581) \n",
      "rec 5.206 reg 0.005\n",
      "epoch 470,loss 3.10104, epsilon 0.33125, rewards: ( e-greedy 0.79066, greedy 2.55223) \n",
      "rec 3.096 reg 0.005\n",
      "epoch 475,loss 7.83080, epsilon 0.32985, rewards: ( e-greedy 0.71160, greedy 2.29700) \n",
      "rec 7.826 reg 0.005\n",
      "epoch 480,loss 12.57731, epsilon 0.32845, rewards: ( e-greedy 1.04044, greedy 2.06730) \n",
      "rec 12.573 reg 0.005\n",
      "epoch 485,loss 10.17728, epsilon 0.32706, rewards: ( e-greedy 1.23639, greedy 1.86057) \n",
      "rec 10.173 reg 0.005\n",
      "epoch 490,loss 20.18388, epsilon 0.32568, rewards: ( e-greedy 1.61275, greedy 2.37452) \n",
      "rec 20.179 reg 0.005\n",
      "epoch 495,loss 3.58645, epsilon 0.32431, rewards: ( e-greedy 1.55148, greedy 2.33706) \n",
      "rec 3.582 reg 0.005\n",
      "epoch 500,loss 2.48451, epsilon 0.32294, rewards: ( e-greedy 1.39633, greedy 2.10336) \n",
      "rec 2.480 reg 0.005\n",
      "Learning curves:\n"
     ]
    },
    {
     "data": {
      "image/png": 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xNZTXiLdvD5w9a/j+VEb0DUWrD1XtqqKZRzOLDIYlEBgSYcTVUF4jXrcu8OxZ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\nfLt0Iv7+lv0sHRt2ZNeLu6jlUYvUzFT2JOyhy+ounEg9AWj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      "text/plain": [
       "<matplotlib.figure.Figure at 0x116ef9e50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Random session examples\n"
     ]
    },
    {
     "data": {
      "image/png": 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welmkUjru3i0ikbB/jwchitY5ikUDly7lba83NvI4fNgHwwAWFjTcuVNEoWC4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rzt7S3Q2GizZUuICxfyDA8vbj/FYm7MzWbdLqgTnycTXcXLZdi8OUhvb15rqM2DEjUPuvvuGHv3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\nEreZ/h8C/xT4iTHmT4CzuLMV4TjOt8aYn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      "text/plain": [
       "<matplotlib.figure.Figure at 0x10df43150>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch 505,loss 4.96383, epsilon 0.32158, rewards: ( e-greedy 1.25670, greedy 1.89302) \n",
      "rec 4.959 reg 0.005\n",
      "epoch 510,loss 5.80681, epsilon 0.32022, rewards: ( e-greedy 1.13103, greedy 1.90372) \n",
      "rec 5.802 reg 0.005\n",
      "epoch 515,loss 21.67333, epsilon 0.31888, rewards: ( e-greedy 1.61793, greedy 1.71335) \n",
      "rec 21.669 reg 0.005\n",
      "epoch 520,loss 4.16324, epsilon 0.31753, rewards: ( e-greedy 1.45613, greedy 1.54201) \n",
      "rec 4.159 reg 0.005\n",
      "epoch 525,loss 5.53925, epsilon 0.31620, rewards: ( e-greedy 1.51052, greedy 1.68781) \n",
      "rec 5.535 reg 0.005\n",
      "epoch 530,loss 2.09149, epsilon 0.31487, rewards: ( e-greedy 1.35947, greedy 1.51903) \n",
      "rec 2.087 reg 0.005\n",
      "epoch 535,loss 11.68379, epsilon 0.31355, rewards: ( e-greedy 1.82352, greedy 1.66713) \n",
      "rec 11.679 reg 0.005\n",
      "epoch 540,loss 8.57061, epsilon 0.31224, rewards: ( e-greedy 2.04117, greedy 1.50041) \n",
      "rec 8.566 reg 0.005\n",
      "epoch 545,loss 3.39190, epsilon 0.31093, rewards: ( e-greedy 1.83705, greedy 1.35037) \n",
      "rec 3.387 reg 0.005\n",
      "epoch 550,loss 3.60306, epsilon 0.30963, rewards: ( e-greedy 1.65335, greedy 1.81534) \n",
      "rec 3.598 reg 0.005\n",
      "epoch 555,loss 3.16394, epsilon 0.30833, rewards: ( e-greedy 1.48801, greedy 1.73380) \n",
      "rec 3.159 reg 0.005\n",
      "epoch 560,loss 1.17040, epsilon 0.30704, rewards: ( e-greedy 1.33921, greedy 1.56042) \n",
      "rec 1.166 reg 0.005\n",
      "epoch 565,loss 19.04321, epsilon 0.30576, rewards: ( e-greedy 2.00529, greedy 1.40438) \n",
      "rec 19.039 reg 0.005\n",
      "epoch 570,loss 7.92066, epsilon 0.30449, rewards: ( e-greedy 2.20476, greedy 1.46394) \n",
      "rec 7.916 reg 0.005\n",
      "epoch 575,loss 5.42697, epsilon 0.30322, rewards: ( e-greedy 2.18428, greedy 2.01755) \n",
      "rec 5.422 reg 0.005\n",
      "epoch 580,loss 2.90483, epsilon 0.30195, rewards: ( e-greedy 1.96586, greedy 1.91579) \n",
      "rec 2.900 reg 0.005\n",
      "epoch 585,loss 3.43872, epsilon 0.30070, rewards: ( e-greedy 1.76927, greedy 1.72421) \n",
      "rec 3.434 reg 0.005\n",
      "epoch 590,loss 4.72352, epsilon 0.29945, rewards: ( e-greedy 1.59234, greedy 1.55179) \n",
      "rec 4.719 reg 0.005\n",
      "epoch 595,loss 4.11303, epsilon 0.29820, rewards: ( e-greedy 1.63311, greedy 1.79661) \n",
      "rec 4.108 reg 0.005\n",
      "epoch 600,loss 5.34157, epsilon 0.29697, rewards: ( e-greedy 1.66980, greedy 1.61695) \n",
      "rec 5.337 reg 0.005\n",
      "epoch 605,loss 12.51995, epsilon 0.29573, rewards: ( e-greedy 2.00282, greedy 1.45526) \n",
      "rec 12.515 reg 0.005\n",
      "epoch 610,loss 2.93007, epsilon 0.29451, rewards: ( e-greedy 1.80254, greedy 1.70973) \n",
      "rec 2.925 reg 0.005\n",
      "epoch 615,loss 22.59417, epsilon 0.29329, rewards: ( e-greedy 2.22228, greedy 2.53876) \n",
      "rec 22.590 reg 0.005\n",
      "epoch 620,loss 3.57977, epsilon 0.29207, rewards: ( e-greedy 2.00005, greedy 2.28488) \n",
      "rec 3.575 reg 0.005\n",
      "epoch 625,loss 4.33436, epsilon 0.29087, rewards: ( e-greedy 1.80005, greedy 2.55639) \n",
      "rec 4.330 reg 0.005\n",
      "epoch 630,loss 5.85191, epsilon 0.28967, rewards: ( e-greedy 1.82004, greedy 2.70075) \n",
      "rec 5.847 reg 0.005\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-60-43d7952d9ce1>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mget_ipython\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun_cell_magic\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mu'time'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34mu''\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34mu'\\nn_epochs = 25000\\n#25k may take hours to train.\\n#consider interrupt early.\\n\\n\\n\\nfor i in range(n_epochs):    \\n    \\n    \\n    #train\\n    update_pool(env,pool,replay_seq_len)\\n    resolver.rng.seed(i)    \\n    loss,avg_reward = train_fun()\\n    \\n    \\n    ##update resolver\\'s epsilon (chance of random action instead of optimal one)\\n    if epoch_counter%1 ==0:\\n        current_epsilon = 0.05 + 0.45*np.exp(-epoch_counter/1000.)\\n        resolver.epsilon.set_value(np.float32(current_epsilon))\\n    \\n    \\n    \\n    ##record current learning progress and show learning curves\\n    if epoch_counter%5 ==0:\\n\\n        ##update learning curves\\n        full_loss, q_loss, l2_penalty, avg_reward_current = evaluation_fun()\\n        ma_reward_current = (1-alpha)*ma_reward_current + alpha*avg_reward_current\\n        score_log[\"expected e-greedy reward\"][epoch_counter] = ma_reward_current\\n        \\n        \\n        \\n        #greedy train\\n        resolver.epsilon.set_value(0)\\n        update_pool(env,pool,replay_seq_len)\\n\\n        avg_reward_greedy = evaluation_fun()[-1]\\n        ma_reward_greedy = (1-alpha)*ma_reward_greedy + alpha*avg_reward_greedy\\n        score_log[\"expected greedy reward\"][epoch_counter] = ma_reward_greedy\\n        \\n        \\n        #back to epsilon-greedy\\n        resolver.epsilon.set_value(np.float32(current_epsilon))\\n        update_pool(env,pool,replay_seq_len)\\n\\n        print(\"epoch %i,loss %.5f, epsilon %.5f, rewards: ( e-greedy %.5f, greedy %.5f) \"%(\\n            epoch_counter,full_loss,current_epsilon,ma_reward_current,ma_reward_greedy))\\n        print(\"rec %.3f reg %.3f\"%(q_loss,l2_penalty))\\n\\n    if epoch_counter %500 ==0:\\n        print(\"Learning curves:\")\\n        score_log.plot()\\n\\n\\n        print(\"Random session examples\")\\n        display_sessions()\\n    \\n\\n        \\n        \\n    epoch_counter  +=1\\n\\n    \\n# Time to drink some coffee!'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m/Users/dmitry/me/anaconda/envs/venv/lib/python2.7/site-packages/IPython/core/interactiveshell.pyc\u001b[0m in \u001b[0;36mrun_cell_magic\u001b[0;34m(self, magic_name, line, cell)\u001b[0m\n\u001b[1;32m   2291\u001b[0m             \u001b[0mmagic_arg_s\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvar_expand\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mline\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstack_depth\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2292\u001b[0m             \u001b[0;32mwith\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbuiltin_trap\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2293\u001b[0;31m                 \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmagic_arg_s\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcell\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2294\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2295\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m<decorator-gen-60>\u001b[0m in \u001b[0;36mtime\u001b[0;34m(self, line, cell, local_ns)\u001b[0m\n",
      "\u001b[0;32m/Users/dmitry/me/anaconda/envs/venv/lib/python2.7/site-packages/IPython/core/magic.pyc\u001b[0m in \u001b[0;36m<lambda>\u001b[0;34m(f, *a, **k)\u001b[0m\n\u001b[1;32m    191\u001b[0m     \u001b[0;31m# but it's overkill for just that one bit of state.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    192\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mmagic_deco\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 193\u001b[0;31m         \u001b[0mcall\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mlambda\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    194\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    195\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mcallable\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/dmitry/me/anaconda/envs/venv/lib/python2.7/site-packages/IPython/core/magics/execution.pyc\u001b[0m in \u001b[0;36mtime\u001b[0;34m(self, line, cell, local_ns)\u001b[0m\n\u001b[1;32m   1165\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1166\u001b[0m             \u001b[0mst\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mclock2\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1167\u001b[0;31m             \u001b[0;32mexec\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcode\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mglob\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlocal_ns\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1168\u001b[0m             \u001b[0mend\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mclock2\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1169\u001b[0m             \u001b[0mout\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m<timed exec>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32m<ipython-input-42-6cc9f1ed662c>\u001b[0m in \u001b[0;36mupdate_pool\u001b[0;34m(env, pool, n_steps)\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      7\u001b[0m     \u001b[0;31m#get interaction sessions\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 8\u001b[0;31m     \u001b[0mobservation_tensor\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0maction_tensor\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mreward_tensor\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0m_\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mis_alive_tensor\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0m_\u001b[0m\u001b[0;34m=\u001b[0m \u001b[0mpool\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minteract\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstep\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mn_steps\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mn_steps\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      9\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     10\u001b[0m     \u001b[0;31m#load them into experience replay environment\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/dmitry/me/anaconda/envs/venv/lib/python2.7/site-packages/agentnet/experiments/openai_gym/pool.pyc\u001b[0m in \u001b[0;36minteract\u001b[0;34m(self, agent_step, n_steps, verbose)\u001b[0m\n\u001b[1;32m     44\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn_steps\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     45\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 46\u001b[0;31m             \u001b[0mactions\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnew_memory_states\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0magent_step\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprev_observations\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprev_memory_states\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     47\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     48\u001b[0m             \u001b[0mnew_observations\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcur_rewards\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mis_done\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minfos\u001b[0m \u001b[0;34m=\u001b[0m\u001b[0;31m \u001b[0m\u001b[0;31m\\\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m<ipython-input-38-d27ee55da7c3>\u001b[0m in \u001b[0;36mstep\u001b[0;34m(observation, prev_memories, batch_size)\u001b[0m\n\u001b[1;32m     11\u001b[0m                                   dtype='float32') \n\u001b[1;32m     12\u001b[0m                          for mem in agent.agent_states]\n\u001b[0;32m---> 13\u001b[0;31m     \u001b[0mres\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mapplier_fun\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobservation\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mprev_memories\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     14\u001b[0m     \u001b[0maction\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mres\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     15\u001b[0m     \u001b[0mmemories\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mres\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/dmitry/me/anaconda/envs/venv/lib/python2.7/site-packages/theano/compile/function_module.pyc\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m    857\u001b[0m         \u001b[0mt0_fn\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtime\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtime\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    858\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 859\u001b[0;31m             \u001b[0moutputs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    860\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    861\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mhasattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfn\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'position_of_error'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/dmitry/me/anaconda/envs/venv/lib/python2.7/site-packages/theano/gof/op.pyc\u001b[0m in \u001b[0;36mrval\u001b[0;34m(p, i, o, n)\u001b[0m\n\u001b[1;32m    909\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mparams\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0mgraph\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mNoParams\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    910\u001b[0m             \u001b[0;31m# default arguments are stored in the closure of `rval`\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 911\u001b[0;31m             \u001b[0;32mdef\u001b[0m \u001b[0mrval\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mp\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mp\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnode_input_storage\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mo\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnode_output_storage\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnode\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    912\u001b[0m                 \u001b[0mr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mp\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mx\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mo\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    913\u001b[0m                 \u001b[0;32mfor\u001b[0m \u001b[0mo\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mnode\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moutputs\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "n_epochs = 25000\n",
    "#25k may take hours to train.\n",
    "#consider interrupt early.\n",
    "\n",
    "\n",
    "\n",
    "for i in range(n_epochs):    \n",
    "    \n",
    "    \n",
    "    #train\n",
    "    update_pool(env,pool,replay_seq_len)\n",
    "    resolver.rng.seed(i)    \n",
    "    loss,avg_reward = train_fun()\n",
    "    \n",
    "    \n",
    "    ##update resolver's epsilon (chance of random action instead of optimal one)\n",
    "    if epoch_counter%1 ==0:\n",
    "        current_epsilon = 0.05 + 0.45*np.exp(-epoch_counter/1000.)\n",
    "        resolver.epsilon.set_value(np.float32(current_epsilon))\n",
    "    \n",
    "    \n",
    "    \n",
    "    ##record current learning progress and show learning curves\n",
    "    if epoch_counter%5 ==0:\n",
    "\n",
    "        ##update learning curves\n",
    "        full_loss, q_loss, l2_penalty, avg_reward_current = evaluation_fun()\n",
    "        ma_reward_current = (1-alpha)*ma_reward_current + alpha*avg_reward_current\n",
    "        score_log[\"expected e-greedy reward\"][epoch_counter] = ma_reward_current\n",
    "        \n",
    "        \n",
    "        \n",
    "        #greedy train\n",
    "        resolver.epsilon.set_value(0)\n",
    "        update_pool(env,pool,replay_seq_len)\n",
    "\n",
    "        avg_reward_greedy = evaluation_fun()[-1]\n",
    "        ma_reward_greedy = (1-alpha)*ma_reward_greedy + alpha*avg_reward_greedy\n",
    "        score_log[\"expected greedy reward\"][epoch_counter] = ma_reward_greedy\n",
    "        \n",
    "        \n",
    "        #back to epsilon-greedy\n",
    "        resolver.epsilon.set_value(np.float32(current_epsilon))\n",
    "        update_pool(env,pool,replay_seq_len)\n",
    "\n",
    "        print(\"epoch %i,loss %.5f, epsilon %.5f, rewards: ( e-greedy %.5f, greedy %.5f) \"%(\n",
    "            epoch_counter,full_loss,current_epsilon,ma_reward_current,ma_reward_greedy))\n",
    "        print(\"rec %.3f reg %.3f\"%(q_loss,l2_penalty))\n",
    "\n",
    "    if epoch_counter %500 ==0:\n",
    "        print(\"Learning curves:\")\n",
    "        score_log.plot()\n",
    "\n",
    "\n",
    "        print(\"Random session examples\")\n",
    "        display_sessions()\n",
    "    \n",
    "\n",
    "        \n",
    "        \n",
    "    epoch_counter  +=1\n",
    "\n",
    "    \n",
    "# Time to drink some coffee!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Evaluating results\n",
    " * Here we plot learning curves and sample testimonials"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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wbWGTSNzRNU1lv31xdPurinLiQMuWOLQTN4Y5ckpZ2EpOUSgUlUc5caCwMMah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\nKxQKhY75fxY6zzahq0nVAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10e0ede10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "score_log.plot(\"final\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Random session examples\n"
     ]
    },
    {
     "data": {
      "image/png": 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M9es9T+/PwPBwAdPTGjgOCAZ5cJz7PjQNSCZ19PXloSgm9uzxgTEG0wTGx1UM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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10b8d0f50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"Random session examples\")\n",
    "display_sessions()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# Submission\n",
    "Here we simply run the OpenAI gym submission code and view scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "resolver.epsilon.set_value(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:gym.envs.registration:Making new env: SpaceInvaders-v0\n",
      "[2016-05-26 17:37:02,292] Making new env: SpaceInvaders-v0\n",
      "INFO:gym.monitoring.monitor:Clearing 2 monitor files from previous run (because force=True was provided)\n",
      "[2016-05-26 17:37:02,316] Clearing 2 monitor files from previous run (because force=True was provided)\n",
      "INFO:gym.monitoring.video_recorder:Starting new video recorder writing to /tmp/AgentNet-simplenet-SpaceInvadersv0-Recording0/openaigym.video.None.31713.video000000.mp4\n",
      "[2016-05-26 17:37:02,329] Starting new video recorder writing to /tmp/AgentNet-simplenet-SpaceInvadersv0-Recording0/openaigym.video.None.31713.video000000.mp4\n",
      "INFO:gym.monitoring.video_recorder:Starting new video recorder writing to /tmp/AgentNet-simplenet-SpaceInvadersv0-Recording0/openaigym.video.None.31713.video000001.mp4\n",
      "[2016-05-26 17:37:05,425] Starting new video recorder writing to /tmp/AgentNet-simplenet-SpaceInvadersv0-Recording0/openaigym.video.None.31713.video000001.mp4\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Episode finished after 613 timesteps\n",
      "Episode finished after 659 timesteps\n",
      "Episode finished after 728 timesteps\n",
      "Episode finished after 805 timesteps\n",
      "Episode finished after 783 timesteps\n",
      "Episode finished after 710 timesteps\n",
      "Episode finished after 653 timesteps\n",
      "Episode finished after 386 timesteps"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:gym.monitoring.video_recorder:Starting new video recorder writing to /tmp/AgentNet-simplenet-SpaceInvadersv0-Recording0/openaigym.video.None.31713.video000008.mp4\n",
      "[2016-05-26 17:37:22,675] Starting new video recorder writing to /tmp/AgentNet-simplenet-SpaceInvadersv0-Recording0/openaigym.video.None.31713.video000008.mp4\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Episode finished after 803 timesteps\n",
      "Episode finished after 710 timesteps"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:gym.monitoring.monitor:Finished writing results. You can upload them to the scoreboard via gym.upload('/tmp/AgentNet-simplenet-SpaceInvadersv0-Recording0')\n",
      "[2016-05-26 17:37:29,013] Finished writing results. You can upload them to the scoreboard via gym.upload('/tmp/AgentNet-simplenet-SpaceInvadersv0-Recording0')\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "\n",
    "save_path = '/tmp/AgentNet-simplenet-SpaceInvadersv0-Recording0'\n",
    "\n",
    "subm_env = gym.make(GAME_TITLE)\n",
    "\n",
    "#starting monitor. This setup does not write videos\n",
    "#subm_env.monitor.start(save_path,lambda i: False,force=True)\n",
    "\n",
    "#this setup does\n",
    "subm_env.monitor.start(save_path,force=True)\n",
    "\n",
    "\n",
    "for i_episode in xrange(10):\n",
    "    \n",
    "    #initial observation\n",
    "    observation = subm_env.reset()\n",
    "    #initial memory\n",
    "    prev_memories = \"zeros\"\n",
    "    \n",
    "    \n",
    "    t = 0\n",
    "    while True:\n",
    "\n",
    "        action,new_memories = step([observation],prev_memories,batch_size=1)\n",
    "        observation, reward, done, info = subm_env.step(action[0])\n",
    "        \n",
    "        prev_memories = new_memories\n",
    "        if done:\n",
    "            print(\"Episode finished after {} timesteps\".format(t+1))\n",
    "            break\n",
    "        t+=1\n",
    "\n",
    "subm_env.monitor.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "gym.monitoring._monitors.clear()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "\n",
    "gym.upload(save_path,\n",
    "           \n",
    "           #this notebook\n",
    "           writeup=<url to my gist>, \n",
    "           \n",
    "           #your api key\n",
    "           api_key=<my_own_api_key>)\n"
   ]
  },
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   "metadata": {
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   "source": [
    "# Once you got it working,\n",
    "Try building a network that maximizes the final score\n",
    "\n",
    "* Moar lasagne stuff: convolutional layers, batch normalization, nonlinearities and so on\n",
    "* Recurrent agent memory layers, GRUMemoryLayer, etc\n",
    "* Different reinforcement learning algorithm (p.e. qlearning_n_step), other parameters\n",
    "* Experience replay pool\n",
    "\n",
    "\n",
    "Look for examples? Try examples/Deep Kung Fu for most of these features\n",
    "\n",
    "\n",
    "You can also try to expand to a different game: \n",
    " * all OpenAI Atari games are already compatible, you only need to change GAME_TITLE\n",
    " * Other discrete action space environments are also accessible this way\n",
    " * For continuous action spaces, either discretize actions or use continuous RL algorithms (e.g. .learning.dpg_n_step)\n",
    " * Adapting to a custom non-OpenAI environment can be done with a simple wrapper\n",
    " \n",
    " \n",
    "__Good luck!__"
   ]
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