{
 "metadata": {
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   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.10-final"
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  "kernelspec": {
   "name": "python3",
   "display_name": "Python 3.7.10 64-bit ('py37': conda)",
   "metadata": {
    "interpreter": {
     "hash": "fbea1422c2cf61ed9c0cfc03f38f71cc9083cc288606edc4170b5309b352ce27"
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 },
 "nbformat": 4,
 "nbformat_minor": 2,
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sys,os\n",
    "from pathlib import Path\n",
    "curr_path = str(Path().absolute())\n",
    "parent_path = str(Path().absolute().parent)\n",
    "sys.path.append(parent_path) # add current terminal path to sys.path\n",
    "\n",
    "import gym\n",
    "import torch\n",
    "import numpy as np\n",
    "import datetime\n",
    "\n",
    "from HierarchicalDQN.agent import HierarchicalDQN\n",
    "from common.plot import plot_rewards\n",
    "from common.utils import save_results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "SEQUENCE = datetime.datetime.now().strftime(\n",
    "    \"%Y%m%d-%H%M%S\")  # obtain current time\n",
    "SAVED_MODEL_PATH = curr_path+\"/saved_model/\"+SEQUENCE+'/'  # path to save model\n",
    "if not os.path.exists(curr_path+\"/saved_model/\"):\n",
    "    os.mkdir(curr_path+\"/saved_model/\")\n",
    "if not os.path.exists(SAVED_MODEL_PATH):\n",
    "    os.mkdir(SAVED_MODEL_PATH)\n",
    "RESULT_PATH = curr_path+\"/results/\"+SEQUENCE+'/'  # path to save rewards\n",
    "if not os.path.exists(curr_path+\"/results/\"):\n",
    "    os.mkdir(curr_path+\"/results/\")\n",
    "if not os.path.exists(RESULT_PATH):\n",
    "    os.mkdir(RESULT_PATH)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "class HierarchicalDQNConfig:\n",
    "    def __init__(self):\n",
    "        self.algo = \"H-DQN\"  # name of algo\n",
    "        self.gamma = 0.95\n",
    "        self.epsilon_start = 1  # start epsilon of e-greedy policy\n",
    "        self.epsilon_end = 0.01\n",
    "        self.epsilon_decay = 500\n",
    "        self.lr = 0.0001  # learning rate\n",
    "        self.memory_capacity = 20000  # Replay Memory capacity\n",
    "        self.batch_size = 64\n",
    "        self.train_eps = 300  # 训练的episode数目\n",
    "        self.target_update = 2  # target net的更新频率\n",
    "        self.eval_eps = 20  # 测试的episode数目\n",
    "        self.device = torch.device(\n",
    "            \"cuda\" if torch.cuda.is_available() else \"cpu\")  # 检测gpu\n",
    "        self.hidden_dim = 256  # dimension of hidden layer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def train(cfg, env, agent):\n",
    "    print('Start to train !')\n",
    "    rewards = []\n",
    "    ma_rewards = []  # moveing average reward\n",
    "    for i_episode in range(cfg.train_eps):\n",
    "        state = env.reset()\n",
    "        done = False\n",
    "        ep_reward = 0\n",
    "        while not done:\n",
    "            goal = agent.set_goal(state)\n",
    "            onehot_goal = agent.to_onehot(goal)\n",
    "            meta_state = state\n",
    "            extrinsic_reward = 0\n",
    "            while not done and goal != np.argmax(state):\n",
    "                goal_state = np.concatenate([state, onehot_goal])\n",
    "                action = agent.choose_action(goal_state)\n",
    "                next_state, reward, done, _ = env.step(action)\n",
    "                ep_reward += reward\n",
    "                extrinsic_reward += reward\n",
    "                intrinsic_reward = 1.0 if goal == np.argmax(\n",
    "                    next_state) else 0.0\n",
    "                agent.memory.push(goal_state, action, intrinsic_reward, np.concatenate(\n",
    "                    [next_state, onehot_goal]), done)\n",
    "                state = next_state\n",
    "                agent.update()\n",
    "        agent.meta_memory.push(meta_state, goal, extrinsic_reward, state, done)\n",
    "        print('Episode:{}/{}, Reward:{}'.format(i_episode+1, cfg.train_eps, ep_reward))\n",
    "        rewards.append(ep_reward)\n",
    "        if ma_rewards:\n",
    "            ma_rewards.append(\n",
    "                0.9*ma_rewards[-1]+0.1*ep_reward)\n",
    "        else:\n",
    "            ma_rewards.append(ep_reward)\n",
    "    print('Complete training！')\n",
    "    return rewards, ma_rewards"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Start to train !\n",
      "Episode:1/300, Reward:25.0\n",
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      "Complete training！\n",
      "results saved!\n"
     ]
    },
    {
     "output_type": "display_data",
     "data": {
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\n"
     },
     "metadata": {}
    }
   ],
   "source": [
    "env = gym.make('CartPole-v0')\n",
    "env.seed(1)\n",
    "cfg = HierarchicalDQNConfig()\n",
    "state_dim = env.observation_space.shape[0]\n",
    "action_dim = env.action_space.n\n",
    "agent = HierarchicalDQN(state_dim, action_dim, cfg)\n",
    "rewards, ma_rewards = train(cfg, env, agent)\n",
    "agent.save(path=SAVED_MODEL_PATH)\n",
    "save_results(rewards, ma_rewards, tag='train', path=RESULT_PATH)\n",
    "plot_rewards(rewards, ma_rewards, tag=\"train\",\n",
    "                algo=cfg.algo, path=RESULT_PATH)"
   ]
  }
 ]
}