{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sys\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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import gym\n",
    "import torch\n",
    "import datetime\n",
    "\n",
    "from SAC.env import NormalizedActions\n",
    "from SAC.agent import SAC\n",
    "from common.utils import save_results, make_dir\n",
    "from common.plot import plot_rewards\n",
    "\n",
    "curr_time = datetime.datetime.now().strftime(\"%Y%m%d-%H%M%S\") # obtain current time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "class SACConfig:\n",
    "    def __init__(self) -> None:\n",
    "        self.algo = 'SAC'\n",
    "        self.env = 'Pendulum-v0'\n",
    "        self.result_path = curr_path+\"/outputs/\" +self.env+'/'+curr_time+'/results/'  # path to save results\n",
    "        self.model_path = curr_path+\"/outputs/\" +self.env+'/'+curr_time+'/models/'  # path to save models\n",
    "        self.train_eps = 300\n",
    "        self.train_steps = 500\n",
    "        self.eval_eps = 50\n",
    "        self.eval_steps = 500\n",
    "        self.gamma = 0.99\n",
    "        self.mean_lambda=1e-3\n",
    "        self.std_lambda=1e-3\n",
    "        self.z_lambda=0.0\n",
    "        self.soft_tau=1e-2\n",
    "        self.value_lr  = 3e-4\n",
    "        self.soft_q_lr = 3e-4\n",
    "        self.policy_lr = 3e-4\n",
    "        self.capacity = 1000000\n",
    "        self.hidden_dim = 256\n",
    "        self.batch_size  = 128\n",
    "        self.device=torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def env_agent_config(cfg,seed=1):\n",
    "    env = NormalizedActions(gym.make(\"Pendulum-v0\"))\n",
    "    env.seed(seed)\n",
    "    action_dim = env.action_space.shape[0]\n",
    "    state_dim  = env.observation_space.shape[0]\n",
    "    agent = SAC(state_dim,action_dim,cfg)\n",
    "    return env,agent"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def train(cfg,env,agent):\n",
    "    print('Start to train !')\n",
    "    print(f'Env: {cfg.env}, Algorithm: {cfg.algo}, Device: {cfg.device}')\n",
    "    rewards  = []\n",
    "    ma_rewards = [] # moveing average reward\n",
    "    for i_ep in range(cfg.train_eps):\n",
    "        state = env.reset()\n",
    "        ep_reward = 0\n",
    "        for i_step in range(cfg.train_steps):\n",
    "            action = agent.policy_net.get_action(state)\n",
    "            next_state, reward, done, _ = env.step(action)\n",
    "            agent.memory.push(state, action, reward, next_state, done)\n",
    "            agent.update()\n",
    "            state = next_state\n",
    "            ep_reward += reward\n",
    "            if done:\n",
    "                break\n",
    "        if (i_ep+1)%10==0:\n",
    "            print(f\"Episode:{i_ep+1}/{cfg.train_eps}, Reward:{ep_reward:.3f}\")\n",
    "        rewards.append(ep_reward)\n",
    "        if ma_rewards:\n",
    "            ma_rewards.append(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": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def eval(cfg,env,agent):\n",
    "    print('Start to eval !')\n",
    "    print(f'Env: {cfg.env}, Algorithm: {cfg.algo}, Device: {cfg.device}')\n",
    "    rewards  = []\n",
    "    ma_rewards = [] # moveing average reward\n",
    "    for i_ep in range(cfg.eval_eps):\n",
    "        state = env.reset()\n",
    "        ep_reward = 0\n",
    "        for i_step in range(cfg.eval_steps):\n",
    "            action = agent.policy_net.get_action(state)\n",
    "            next_state, reward, done, _ = env.step(action)\n",
    "            state = next_state\n",
    "            ep_reward += reward\n",
    "            if done:\n",
    "                break\n",
    "        if (i_ep+1)%10==0:\n",
    "            print(f\"Episode:{i_ep+1}/{cfg.train_eps}, Reward:{ep_reward:.3f}\")\n",
    "        rewards.append(ep_reward)\n",
    "        if ma_rewards:\n",
    "            ma_rewards.append(0.9*ma_rewards[-1]+0.1*ep_reward)\n",
    "        else:\n",
    "            ma_rewards.append(ep_reward) \n",
    "    print('Complete evaling！')\n",
    "    return rewards, ma_rewards\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "ename": "DeprecatedEnv",
     "evalue": "Env Pendulum-v0 not found (valid versions include ['Pendulum-v1'])",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[0;32m~/anaconda3/envs/py37/lib/python3.7/site-packages/gym/envs/registration.py\u001b[0m in \u001b[0;36mspec\u001b[0;34m(self, path)\u001b[0m\n\u001b[1;32m    157\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 158\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0menv_specs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mid\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    159\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyError\u001b[0m: 'Pendulum-v0'",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[0;31mDeprecatedEnv\u001b[0m                             Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-7-91b1038013e4>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m     \u001b[0;31m# train\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m     \u001b[0menv\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0magent\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0menv_agent_config\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcfg\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mseed\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[0m\u001b[1;32m      6\u001b[0m     \u001b[0mrewards\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mma_rewards\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtrain\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcfg\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0menv\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0magent\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      7\u001b[0m     \u001b[0mmake_dir\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcfg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mresult_path\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcfg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmodel_path\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m<ipython-input-4-040773221550>\u001b[0m in \u001b[0;36menv_agent_config\u001b[0;34m(cfg, seed)\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0menv_agent_config\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcfg\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mseed\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[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m     \u001b[0menv\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mNormalizedActions\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgym\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmake\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Pendulum-v0\"\u001b[0m\u001b[0;34m)\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      3\u001b[0m     \u001b[0menv\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mseed\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mseed\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m     \u001b[0maction_dim\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0menv\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0maction_space\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m     \u001b[0mstate_dim\u001b[0m  \u001b[0;34m=\u001b[0m \u001b[0menv\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mobservation_space\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/py37/lib/python3.7/site-packages/gym/envs/registration.py\u001b[0m in \u001b[0;36mmake\u001b[0;34m(id, **kwargs)\u001b[0m\n\u001b[1;32m    233\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    234\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mmake\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mid\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 235\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0mregistry\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmake\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mid\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\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    236\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    237\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/py37/lib/python3.7/site-packages/gym/envs/registration.py\u001b[0m in \u001b[0;36mmake\u001b[0;34m(self, path, **kwargs)\u001b[0m\n\u001b[1;32m    126\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    127\u001b[0m             \u001b[0mlogger\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minfo\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Making new env: %s\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpath\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 128\u001b[0;31m         \u001b[0mspec\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mspec\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpath\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    129\u001b[0m         \u001b[0menv\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mspec\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmake\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    130\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0menv\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/py37/lib/python3.7/site-packages/gym/envs/registration.py\u001b[0m in \u001b[0;36mspec\u001b[0;34m(self, path)\u001b[0m\n\u001b[1;32m    185\u001b[0m                 raise error.DeprecatedEnv(\n\u001b[1;32m    186\u001b[0m                     \"Env {} not found (valid versions include {})\".format(\n\u001b[0;32m--> 187\u001b[0;31m                         \u001b[0mid\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmatching_envs\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    188\u001b[0m                     )\n\u001b[1;32m    189\u001b[0m                 )\n",
      "\u001b[0;31mDeprecatedEnv\u001b[0m: Env Pendulum-v0 not found (valid versions include ['Pendulum-v1'])"
     ]
    }
   ],
   "source": [
    "if __name__ == \"__main__\":\n",
    "    cfg=SACConfig()\n",
    "    \n",
    "    # train\n",
    "    env,agent = env_agent_config(cfg,seed=1)\n",
    "    rewards, ma_rewards = train(cfg, env, agent)\n",
    "    make_dir(cfg.result_path, cfg.model_path)\n",
    "    agent.save(path=cfg.model_path)\n",
    "    save_results(rewards, ma_rewards, tag='train', path=cfg.result_path)\n",
    "    plot_rewards(rewards, ma_rewards, tag=\"train\",\n",
    "                 algo=cfg.algo, path=cfg.result_path)\n",
    "    # eval\n",
    "    env,agent = env_agent_config(cfg,seed=10)\n",
    "    agent.load(path=cfg.model_path)\n",
    "    rewards,ma_rewards = eval(cfg,env,agent)\n",
    "    save_results(rewards,ma_rewards,tag='eval',path=cfg.result_path)\n",
    "    plot_rewards(rewards,ma_rewards,tag=\"eval\",env=cfg.env,algo = cfg.algo,path=cfg.result_path)\n"
   ]
  }
 ],
 "metadata": {
  "interpreter": {
   "hash": "fe38df673a99c62a9fea33a7aceda74c9b65b12ee9d076c5851d98b692a4989a"
  },
  "kernelspec": {
   "display_name": "Python 3.7.10 64-bit ('mujoco': conda)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "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"
  },
  "metadata": {
   "interpreter": {
    "hash": "fd81e6a9e450d5c245c1a0b5da0b03c89c450f614a13afa2acb1654375922756"
   }
  },
  "orig_nbformat": 2
 },
 "nbformat": 4,
 "nbformat_minor": 2
}