week08_pomdp/homework_common_part2.ipynb

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        "### This is a common homework assignment for both frameworks\n",
        "\n",
        "This week's assignment appears to be unusually grandeur, so please read submission/grading guidelines before you upload it for review.\n",
        "\n",
        "__Submisson__: To ease mutual pain, please submit\n",
        "- Some kind of readable report with links to your evaluations, gym uploads, investigation results, etc.\n",
        "- Explicitly state that you took on a bonus task and where to find it [to make sure it is found and graded].\n",
        "\n",
        "__Grading__: The main purpose (and source of points) for this notebook is your investigation, not squeezing out average rewards from random environments. \n",
        "\n",
        "Getting near/above state of the art performance on one particular game will earn you some bonus points, but you can get much more by digging deeper into what makes the algorithms tick and how they compare to one another.\n",
        "\n",
        "Okay, now brace yourselves, here comes an assignment!\n",
        "\n",
        "#### 7.2 Deep kung-fu (3 pts)\n",
        "\n",
        "Implement and train recurrent actor-critic on `KungFuMaster-v0` env in the second notebook. Try to get a score of >=20k.\n",
        "\n",
        "Please __upload your algorithm to [gym leaderboard]__(https://gym.openai.com/envs/KungFuMaster-v0) and submit the link to your eval!\n",
        "\n",
        "__[bonus points]__ +1 point per each +5k average reward over 20k baseline (25k = +1, 30k = +2, ...)\n",
        "\n",
        "\n",
        "#### 7.3 Comparing what we know (7+ pts)\n",
        "\n",
        "_Please read this assignment carefully._\n",
        "\n",
        "Choose a partially-observable environment for experimentation out of [atari](https://gym.openai.com/envs#atari), [doom](https://gym.openai.com/envs#doom) or [pygame](https://gym.openai.com/envs#pygame) catalogue (if you really want to try some other pomdp, feel free to proceed at your own risk).\n",
        "\n",
        "Not all atari environements are bug free and these minor bugs can hurt learning performance. \n",
        "We recommend to pick one of those:\n",
        "* [Assault-v0](https://gym.openai.com/envs/Assault-v0) \n",
        "* [DoomDefendCenter-v0](https://gym.openai.com/envs/DoomDefendCenter-v0) (use env code from [this](https://github.com/yandexdataschool/Practical_RL/blob/master/week4/Seminar4.2_conv_agent.ipynb) notebook)\n",
        "* [RoadRunner-v0](https://gym.openai.com/envs/RoadRunner-v0)\n",
        "\n",
        "Unless you have aesthetical preference, we would appreciate if you chose env out of recommended ones by `random.choice`.\n",
        "\n",
        "__Your task__ is to implement DRQN and A3C (seminar code may be reused) and apply them __both__ to the environement of your choice. Then compare them on the chosen game (convergence / sample efficiency / final performance).\n",
        "\n",
        "\n",
        "* It's probably a good idea to compare a3c vs q-learning with similar network complexity. \n",
        "* Also remember that you can only use large experience replay for 1-step q-learning\n",
        "\n",
        "\n",
        "__Tips__:\n",
        "Your new environment may require some tuning before it gives up to your agent:\n",
        "\n",
        "\n",
        "* Different preprocessing. Mostly cropping.\n",
        " * In some cases, even larger screen size or colorization. \n",
        " * View resulting image to figure that out.\n",
        "\n",
        "\n",
        "* Reward scaling. \n",
        " * Kung-fu used `rewards=replay.rewards/100.` because you got +100 per success.\n",
        " * Just avoid training on raw +100 rewards or it's gonna blow up mean squared error.\n",
        "\n",
        "\n",
        "* Deterministic/FrameSkip\n",
        " * For doom/pygame/custom, use frameskip to speed up learning\n",
        "   * ```from gym.wrappers import SkipWrapper```\n",
        "   * ```env = SkipWrapper(how_many_frames_to_skip)(your_env)``` in your make_env\n",
        " \n",
        " * For atari only, consider __training__ on deterministic version of environment\n",
        "   * Works by appending Deterministic to env name: `AssaultDeterministic-v0`, `KungFuMasterDeterministic-v0`\n",
        "   * Expect faster training due to less variance.\n",
        "   * You still need to __switch back to normal env for evaluation__ (there's no leaderbord for deterministic envs)\n",
        "\n",
        "* Knowledge transfer\n",
        "   * If you want to switch network mid-game, you are recommended to use some pre-trained layers\n",
        "   * At minimum, save convolutional weights and re-use them in every new architecture using fine-tuning\n",
        "   * At it's darkest, [soft-targets](http://www.kdnuggets.com/2015/05/dark-knowledge-neural-network.html), [policy distillation](https://arxiv.org/pdf/1511.06295.pdf), [net2net](https://arxiv.org/abs/1511.05641) or similar __[bonus points]__.\n",
        "\n",
        "\n",
        "\n",
        "#### For the curious\n",
        "- __[4+ bonus points]__ Implement attentive model for DQRN/A3C (see lecture slides for implementation details). How does it compare to the vanilla architecture? \n",
        "* __[2+ bonus points]__ If you have any q-learning modiffications from week5 (double q-l, prioritized replay, etc.), they are most welcome here!\n",
        "* __[2+ bonus points]__ How different memory amounts and types (LSTM / GRU / RNN / combo / custom) affects DRQN / A2C performance? Try to find optimal configuration.\n",
        "- __[2+ bonus points]__ No one said l2 loss is perfect. Implement Huber or MAE loss for DRQN and/or A2C critic and compare it's performance on the game of your choice (pass proper `loss_function` to `get_elementwise_objective()`) .\n",
        "- __[1++ bonus points]__ Does it help to add recurrent units when in MDP scenario, e.g fully observable \"CartPole-v0\"?  How about if you only give it access to position observations? Only speed observations? Try that out!\n",
        "- __[4+ bonus points]__ See the very end of this notebook. Some of the games (right side) benefit a lot from additional LSTM memory. But others (left side) do not. That is interesting. Pick up one or several games from the left side and try to figure out why A2C performance decreases when adding LSTM to feadforward architecture?\n",
        "\n",
        "#### Bonus: Neural Maps (a LOT of points if successful)\n",
        "\n",
        "Pick up either [DoomMyWayHome-v0](https://gym.openai.com/envs/DoomMyWayHome-v0) or  [RaycastMaze-v0](https://gym.openai.com/envs/RaycastMaze-v0) and apply Neural Map to it. Main details of Neural Map are given in lecture slides and you could also benefit from reading [Neural Map article](https://arxiv.org/abs/1702.08360). \n",
        "\n",
        "[hse/ysda] Feel free to ask Pavel Shvechikov / Fedor Ratnikov any questions, guidance and clarifications on the topic.\n",
        "\n",
        "This block is highly experimental and may be connected with some additional difficulties compared to main track. With some brief description of you work you could get additional points\n",
        "\n",
        "_Scoring points are not pre-determined for this task because we're uncertain of implementation complexity._\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "__You can use the following template for DRQN implementation or throw it away entirely__"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": true
      },
      "outputs": [],
      "source": [
        "N_ACTIONS = env.action_space.n",
        "\n",
        "OBS_SHAPE = env.observation_space.shape",
        "\n",
        "OBS_CHANNELS, OBS_HEIGHT, OBS_WIDTH = OBS_SHAPE",
        "\n",
        "\n",
        "N_SIMULTANEOUS_GAMES = 2  # this is also known as number of agents in exp_replay_pool",
        "\n",
        "MAX_POOL_SIZE = 1000",
        "\n",
        "REPLAY_SIZE = 100",
        "\n",
        "SEQ_LENGTH = 15",
        "\n",
        "\n",
        "N_POOL_UPDATES = 1",
        "\n",
        "EVAL_EVERY_N_ITER = 10",
        "\n",
        "N_EVAL_GAMES = 1",
        "\n",
        "\n",
        "N_FRAMES_IN_BUFFER = 4  # number of consequent frames to feed in CNN"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": true
      },
      "outputs": [],
      "source": [
        "observation_layer = InputLayer((None,) + OBS_SHAPE)",
        "\n",
        "prev_wnd = InputLayer((None, N_FRAMES_IN_BUFFER) + OBS_SHAPE)",
        "\n",
        "new_wnd = WindowAugmentation(observation_layer, prev_wnd)",
        "\n",
        "wnd_reshape = reshape(",
        "\n",
        "    new_wnd, [-1,  N_FRAMES_IN_BUFFER * OBS_CHANNELS, OBS_HEIGHT, OBS_WIDTH])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": true
      },
      "outputs": [],
      "source": [
        "conv1 = Conv2DLayer(wnd_reshape, num_filters=32, filter_size=(8, 8), stride=4)",
        "\n",
        "conv2 = Conv2DLayer(conv1, num_filters=64, filter_size=(4, 4), stride=2)",
        "\n",
        "conv3 = Conv2DLayer(conv2, num_filters=64, filter_size=(3, 3), stride=1)",
        "\n",
        "dense1 = DenseLayer(conv3, num_units=512)",
        "\n",
        "qvalues_layer = DenseLayer(dense1, num_units=N_ACTIONS, nonlinearity=None)",
        "\n",
        "action_layer = EpsilonGreedyResolver(qvalues_layer)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": true
      },
      "outputs": [],
      "source": [
        "targetnet = TargetNetwork(qvalues_layer)",
        "\n",
        "qvalues_old_layer = targetnet.output_layers"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": true
      },
      "outputs": [],
      "source": [
        "agent = Agent(observation_layers=observation_layer,",
        "\n",
        "              policy_estimators=(qvalues_layer, qvalues_old_layer),",
        "\n",
        "              action_layers=action_layer,",
        "\n",
        "              agent_states={new_wnd: prev_wnd})",
        "\n",
        "pool = EnvPool(agent, make_env=make_env,",
        "\n",
        "               n_games=N_SIMULTANEOUS_GAMES, max_size=MAX_POOL_SIZE)",
        "\n",
        "replay = pool.experience_replay.sample_session_batch(REPLAY_SIZE)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": true
      },
      "outputs": [],
      "source": [
        "# .get_sessions() returns env_states, observations, agent_states, actions, policy_estimators",
        "\n",
        "(qvalues_seq, old_qvalues_seq) = agent.get_sessions(",
        "\n",
        "    replay, session_length=SEQ_LENGTH, experience_replay=True)[-1]",
        "\n",
        "elwise_mse_loss = qlearning.get_elementwise_objective(",
        "\n",
        "    qvalues_seq,",
        "\n",
        "    replay.actions[0],",
        "\n",
        "    replay.rewards,",
        "\n",
        "    replay.is_alive,",
        "\n",
        "    qvalues_target=old_qvalues_seq,",
        "\n",
        "    gamma_or_gammas=0.999,",
        "\n",
        "    n_steps=1",
        "\n",
        ")",
        "\n",
        "loss = elwise_mse_loss.sum() / replay.is_alive.sum()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": true
      },
      "outputs": [],
      "source": [
        "weights = lasagne.layers.get_all_params(action_layer, trainable=True)",
        "\n",
        "updates = lasagne.updates.adam(loss, weights, learning_rate=1e-4)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": true
      },
      "outputs": [],
      "source": [
        "train_step = theano.function([], loss, updates=updates)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "***\n",
        "***\n",
        "***"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### A3C feadforward vs A3C LSTM on Atari games"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 62,
      "metadata": {
        "collapsed": true
      },
      "outputs": [],
      "source": [
        "a3c_ff = [518.4, 263.9, 5474.9, 22140.5, 4474.5, 911091.0, 970.1, 12950.0, 22707.9, 817.9, 35.1, 59.8, 681.9, 3755.8, 7021.0, 112646.0, 56533.0, 113308.4, -0.1, -82.5, 18.8, 0.1, 190.5, 10022.8, 303.5, 32464.1, -2.8, 541.0, 94.0,",
        "\n",
        "          5560.0, 28819.0, 67.0, 653.7, 10476.1, 52894.1, -78.5, 5.6, 206.9, 15148.8, 12201.8, 34216.0, 32.8, 2355.4, -10911.1, 1956.0, 15730.5, 138218.0, -9.7, -6.3, 12679.0, 156.3, 74705.7, 23.0, 331628.1, 17244.0, 7157.5, 24622.0]",
        "\n",
        "a3c_lstm = [945.3, 173.0, 14497.9, 17244.5, 5093.1, 875822.0, 932.8, 20760.0, 24622.2, 862.2, 41.8, 37.3, 766.8, 1997.0, 10150.0, 138518.0, 233021.5, 115201.9, 0.1, -82.5, 22.6, 0.1, 197.6, 17106.8, 320.0, 28889.5, -1.7, 613.0,",
        "\n",
        "            125.0, 5911.4, 40835.0, 41.0, 850.7, 12093.7, 74786.7, -135.7, 10.7, 421.1, 21307.5, 6591.9, 73949.0, 2.6, 1326.1, -14863.8, 1936.4, 23846.0, 164766.0, -8.3, -6.4, 27202.0, 144.2, 105728.7, 25.0, 470310.5, 18082.0, 5615.5, 23519.0]",
        "\n",
        "game_names = \"Alien Amidar Assault Asterix Asteroids Atlantis Bank Battle Beam Berzerk Bowling Boxing Breakout Centipede Chopper Crazy Defender Demon Double Enduro Fishing Freeway Frostbite Gopher Gravitar H.E.R.O. Ice James Kangaroo Krull Kung-Fu Montezuma's Ms. Name Phoenix Pit Pong Private Q*Bert River Road Robotank Seaquest Skiing Solaris Space Star Surround Tennis Time Tutankham Up Venture Video Wizard Yars Zaxxon\".split(",
        "\n",
        "    \" \")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 63,
      "metadata": {
        "collapsed": true
      },
      "outputs": [],
      "source": [
        "score_difference = np.array(a3c_lstm) - np.array(a3c_ff)",
        "\n",
        "idxs = np.argsort(score_difference)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 65,
      "metadata": {},
      "outputs": [
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          "data": {
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BzxCyl3vdvg6QFQV3MrNdCedmb8KQ1fvNbM+sJ1abcff5ZvZoptkP6EPojTWd0NNxW8Jc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SJj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\nD6lwIZ0LMZmFAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x1117b0510>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "plt.figure(figsize=(15, 6))",
        "\n",
        "plt.plot(np.sort(score_difference))",
        "\n",
        "plt.yscale(\"symlog\")",
        "\n",
        "plt.xticks(np.arange(len(game_names)), np.array(",
        "\n",
        "    game_names)[idxs], rotation='vertical')",
        "\n",
        "plt.grid()",
        "\n",
        "plt.title(\"Comparison A3C on atari games: with and without LSTM memory\")",
        "\n",
        "plt.ylabel(\"Difference between A3C_LSTM and A3C_FeadForward scores\")"
      ]
    }
  ],
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