mxnet_A2C_hybrid.ipynb

{
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  "metadata": {
    "colab": {
      "name": "mxnet_A2C.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/skywalker0803r/mxnet_course/blob/master/mxnet_A2C_hybrid.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RnYzFg5rjMn1",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#!pip install mxnet\n",
        "#!pip install box2d-py\n",
        "import numpy as np \n",
        "import mxnet as mx \n",
        "from mxnet import gluon,autograd,nd\n",
        "import mxnet.ndarray as F\n",
        "from mxnet.gluon import nn\n",
        "from mxnet.gluon.loss import L2Loss\n",
        "import gym\n",
        "from mxnet import init\n",
        "import matplotlib.pyplot as plt"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "A3myl1gY8ZfR",
        "colab_type": "text"
      },
      "source": [
        "# hyperparams"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lTSstV4u8ZqP",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "env_name = \"CartPole-v1\"\n",
        "solved_reward = 500 \n",
        "log_interval = 10\n",
        "lr = 0.001\n",
        "gamma = 0.99               "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8CTamjHV-Qso",
        "colab_type": "text"
      },
      "source": [
        "# env"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rSfKiLCq-Pnm",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        },
        "outputId": "0da6076e-1676-49fc-fbac-6b635c986ef8"
      },
      "source": [
        "env = gym.make(env_name)\n",
        "state_dim = env.observation_space.shape[0]\n",
        "action_dim = env.action_space.n\n",
        "print(state_dim,action_dim)"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "4 2\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "focw4c9foUMv",
        "colab_type": "text"
      },
      "source": [
        "# ActorCritic model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dP3NWvqrm7m5",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "class ActorCritic_Discrete(nn.HybridBlock):\n",
        "  def __init__(self, actions_dim):\n",
        "    super().__init__()\n",
        "    with self.name_scope():\n",
        "      self.fc1 = nn.Dense(128,activation='tanh')\n",
        "      self.fc2 = nn.Dense(128,activation='relu')\n",
        "      self.action_pred = nn.Dense(actions_dim)\n",
        "      self.value_pred = nn.Dense(1)\n",
        "  def hybrid_forward(self,F,x):\n",
        "    x = self.fc1(x)\n",
        "    x = self.fc2(x)\n",
        "    probs = self.action_pred(x)\n",
        "    values = self.value_pred(x)\n",
        "    return F.softmax(probs), values"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2r8G5ToT82bn",
        "colab_type": "text"
      },
      "source": [
        "# forward test"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_s6v6sGh83-7",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 208
        },
        "outputId": "db840985-bcd9-445b-d79a-f862b934db3d"
      },
      "source": [
        "state = env.reset()\n",
        "state = nd.array([state])\n",
        "\n",
        "a = ActorCritic_Discrete(action_dim)\n",
        "b = ActorCritic_Discrete(action_dim)\n",
        "\n",
        "a.initialize(init=init.Xavier())\n",
        "b.initialize(init=init.Xavier())\n",
        "\n",
        "a.hybridize()\n",
        "b.hybridize()\n",
        "\n",
        "print(a(state))\n",
        "print(b(state))"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[\n",
            "[[0.49933335 0.5006667 ]]\n",
            "<NDArray 1x2 @cpu(0)>, \n",
            "[[0.00294055]]\n",
            "<NDArray 1x1 @cpu(0)>]\n",
            "[\n",
            "[[0.49908295 0.5009171 ]]\n",
            "<NDArray 1x2 @cpu(0)>, \n",
            "[[0.00337218]]\n",
            "<NDArray 1x1 @cpu(0)>]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GrdpG5Gf88Cz",
        "colab_type": "text"
      },
      "source": [
        "# copy params test"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RPUV_KIL88iv",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 208
        },
        "outputId": "1b1c248d-3b29-47e4-e408-cd409283a888"
      },
      "source": [
        "a.save_parameters(\"temp\")\n",
        "b.load_parameters(\"temp\")\n",
        "\n",
        "print(a(state))\n",
        "print(b(state))"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[\n",
            "[[0.49933335 0.5006667 ]]\n",
            "<NDArray 1x2 @cpu(0)>, \n",
            "[[0.00294055]]\n",
            "<NDArray 1x1 @cpu(0)>]\n",
            "[\n",
            "[[0.49933335 0.5006667 ]]\n",
            "<NDArray 1x2 @cpu(0)>, \n",
            "[[0.00294055]]\n",
            "<NDArray 1x1 @cpu(0)>]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "H1f5XdxQ9A3l",
        "colab_type": "text"
      },
      "source": [
        "# model save test"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UAnSkyqf9BZe",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "a.export('test_save_model')\n",
        "b.export('test_save_model')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5FojKwxVsKR1",
        "colab_type": "text"
      },
      "source": [
        "# initialize"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8GQS6tmcpjI_",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "loss = gluon.loss.L2Loss()\n",
        "\n",
        "model = ActorCritic_Discrete(action_dim)\n",
        "model.collect_params().initialize(mx.init.Xavier())\n",
        "model.hybridize()\n",
        "\n",
        "optimizer = gluon.Trainer(model.collect_params(),'Adam',{'learning_rate':lr})"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "83S-IzFfsPFT",
        "colab_type": "text"
      },
      "source": [
        "# main loop"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uiHLjqnmrvk7",
        "colab_type": "code",
        "outputId": "29e9282e-b49f-422f-8d0a-8ccfe98e8a3c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 571
        }
      },
      "source": [
        "all_rewards = []\n",
        "avg_rewards = []\n",
        "\n",
        "for episode in range(1000):\n",
        "\n",
        "  # reset placeholder\n",
        "  rewards = []\n",
        "  values = []\n",
        "  actions = []\n",
        "  heads = []\n",
        "  state = env.reset()\n",
        "  total = 0\n",
        "  \n",
        "  with autograd.record():\n",
        "    for learning_step in range(1000):\n",
        "      \n",
        "      # nd_array state\n",
        "      state = nd.array(np.expand_dims(state, 0))\n",
        "      \n",
        "      # choose action and do action on env\n",
        "      prob, value = model(state)\n",
        "      action, logp = mx.nd.sample_multinomial(prob, get_prob=True)\n",
        "      action = action.asnumpy()[0].astype(np.int64)\n",
        "      next_state, reward, done, info = env.step(action)\n",
        "      \n",
        "      # record data\n",
        "      total += reward\n",
        "      rewards.append(reward)\n",
        "      actions.append(action)\n",
        "      values.append(value)\n",
        "      heads.append(logp)\n",
        "\n",
        "      # is game over?  \n",
        "      if done:\n",
        "        break\n",
        "      else:\n",
        "        state = next_state\n",
        "    \n",
        "    # record\n",
        "    all_rewards.append(total)\n",
        "    avg_rewards.append(np.mean(all_rewards[-10:]))\n",
        "    \n",
        "    # print infomation\n",
        "    if episode % 10 == 0:\n",
        "      print(episode,avg_rewards[-1])\n",
        "    \n",
        "    # if solved break\n",
        "    if (total >= solved_reward)&(avg_rewards[-1] >= solved_reward*0.9):\n",
        "      print(episode,total,avg_rewards[-1])\n",
        "      break\n",
        "        \n",
        "    # reverse accumulate and normalize rewards\n",
        "    R = 0\n",
        "    for i in range(len(rewards) - 1, -1, -1):\n",
        "      R = rewards[i] + gamma * R\n",
        "      rewards[i] = R\n",
        "    rewards = np.array(rewards)\n",
        "    rewards -= rewards.mean()\n",
        "    rewards /= rewards.std() + np.finfo(rewards.dtype).eps\n",
        "\n",
        "    # critic loss\n",
        "    L = sum([loss(value, mx.nd.array([r])) for r, value in zip(rewards, values)])\n",
        "    final_nodes = [L]\n",
        "    \n",
        "    # actor loss\n",
        "    for logp, r, v in zip(heads, rewards, values):\n",
        "      reward = r - v.asnumpy()[0, 0]\n",
        "      final_nodes.append(logp * (-reward))\n",
        "  \n",
        "  # loss backward\n",
        "  autograd.backward(final_nodes)\n",
        "  optimizer.step(len(final_nodes))"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0 10.0\n",
            "10 21.1\n",
            "20 37.3\n",
            "30 42.4\n",
            "40 37.7\n",
            "50 40.1\n",
            "60 57.6\n",
            "70 70.2\n",
            "80 103.7\n",
            "90 96.1\n",
            "100 67.1\n",
            "110 78.8\n",
            "120 132.8\n",
            "130 157.6\n",
            "140 168.5\n",
            "150 183.8\n",
            "160 200.0\n",
            "170 240.2\n",
            "180 255.2\n",
            "190 309.9\n",
            "200 258.6\n",
            "210 352.2\n",
            "220 283.6\n",
            "230 288.2\n",
            "240 353.5\n",
            "250 383.7\n",
            "260 392.7\n",
            "270 417.7\n",
            "273 500.0 453.4\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VZ-QK4VtuVkY",
        "colab_type": "code",
        "outputId": "0b262b09-a0c9-4ea2-825f-d0d2ec2e84c0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        }
      },
      "source": [
        "plt.plot(all_rewards)\n",
        "plt.plot(avg_rewards)\n",
        "plt.xlabel('episode')\n",
        "plt.show()"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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cjW4ehTCxH859IdEG7Xo5MXAh77ngyxz1Vmci9/GSJvdV8kS8oHqc2WAFUuqs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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0-W8ObQIAGZV",
        "colab_type": "text"
      },
      "source": [
        "# save model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GJ_rWfn8mv9x",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "model.export('{}_model'.format(env_name))\n",
        "del model"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wJfVQ0DlAOjE",
        "colab_type": "text"
      },
      "source": [
        "# load model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EHc9mCJLANj1",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 75
        },
        "outputId": "9ec729b1-1a00-4a20-c34a-13a01b853707"
      },
      "source": [
        "deserialized_net = nn.SymbolBlock.imports(\"{}_model-symbol.json\".format(env_name), \n",
        "                                          ['data'], \n",
        "                                          \"{}_model-0000.params\".format(env_name))"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/mxnet/gluon/block.py:1159: UserWarning: Cannot decide type for the following arguments. Consider providing them as input:\n",
            "\tdata: None\n",
            "  input_sym_arg_type = in_param.infer_type()[0]\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gqjADmQrAWs7",
        "colab_type": "text"
      },
      "source": [
        "# test model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "l3WwGtETApz1",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "env = gym.make(env_name)\n",
        "model = deserialized_net"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lcGNF-FMATvj",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 208
        },
        "outputId": "ec2d07fe-ac3a-4d68-e362-d31a47de0113"
      },
      "source": [
        "for i_episode in range(10):\n",
        "  state = env.reset()\n",
        "  R = 0\n",
        "  for t in range(1000):\n",
        "    probs,values = model(nd.array([state]))\n",
        "    action = np.argmax(probs.asnumpy())\n",
        "    state, reward, done, info = env.step(action)\n",
        "    R += reward\n",
        "    if done:\n",
        "      break            \n",
        "  print(i_episode,R)"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0 500.0\n",
            "1 500.0\n",
            "2 500.0\n",
            "3 500.0\n",
            "4 500.0\n",
            "5 500.0\n",
            "6 432.0\n",
            "7 500.0\n",
            "8 500.0\n",
            "9 500.0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zGP2J-URAgx4",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}