easy-rl/codes/PPO/train.ipynb

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  {
   "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",
    "from PPO.agent import PPO\n",
    "from common.plot import plot_rewards\n",
    "from common.utils import save_results,make_dir\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 PPOConfig:\n",
    "    def __init__(self) -> None:\n",
    "        self.env = 'CartPole-v0'\n",
    "        self.algo = 'PPO'\n",
    "        self.result_path = curr_path+\"/results/\" +self.env+'/'+curr_time+'/results/'  # path to save results\n",
    "        self.model_path = curr_path+\"/results/\" +self.env+'/'+curr_time+'/models/'  # path to save models\n",
    "        self.train_eps = 200 # max training episodes\n",
    "        self.test_eps = 50\n",
    "        self.batch_size = 5\n",
    "        self.gamma=0.99\n",
    "        self.n_epochs = 4\n",
    "        self.actor_lr = 0.0003\n",
    "        self.critic_lr = 0.0003\n",
    "        self.gae_lambda=0.95\n",
    "        self.policy_clip=0.2\n",
    "        self.hidden_dim = 256\n",
    "        self.update_fre = 20 # frequency of agent update\n",
    "        self.device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\") # check gpu"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def env_agent_config(cfg,seed=1):\n",
    "    env = gym.make(cfg.env)  \n",
    "    env.seed(seed)\n",
    "    state_dim = env.observation_space.shape[0]\n",
    "    action_dim = env.action_space.n\n",
    "    agent = PPO(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 = [] # moving average rewards\n",
    "    running_steps = 0\n",
    "    for i_ep in range(cfg.train_eps):\n",
    "        state = env.reset()\n",
    "        done = False\n",
    "        ep_reward = 0\n",
    "        while not done:\n",
    "            action, prob, val = agent.choose_action(state)\n",
    "            state_, reward, done, _ = env.step(action)\n",
    "            running_steps += 1\n",
    "            ep_reward += reward\n",
    "            agent.memory.push(state, action, prob, val, reward, done)\n",
    "            if running_steps % cfg.update_fre == 0:\n",
    "                agent.update()\n",
    "            state = state_\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",
    "        if (i_ep+1)%10==0:\n",
    "            print(f\"Episode:{i_ep+1}/{cfg.train_eps}, Reward:{ep_reward:.3f}\")\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 = [] # moving average rewards\n",
    "    for i_ep in range(cfg.test_eps):\n",
    "        state = env.reset()\n",
    "        done = False\n",
    "        ep_reward = 0\n",
    "        while not done:\n",
    "            action, prob, val = agent.choose_action(state)\n",
    "            state_, reward, done, _ = env.step(action)\n",
    "            ep_reward += reward\n",
    "            state = state_\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",
    "        if (i_ep+1)%10==0:\n",
    "            print(f\"Episode:{i_ep+1}/{cfg.train_eps}, Reward:{ep_reward:.3f}\")\n",
    "    print('Complete evaling！')\n",
    "    return rewards,ma_rewards"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Start to train !\n",
      "Env:CartPole-v0, Algorithm:PPO, Device:cuda\n",
      "Episode:10/200, Reward:15.000\n",
      "Episode:20/200, Reward:9.000\n",
      "Episode:30/200, Reward:20.000\n",
      "Episode:40/200, Reward:17.000\n",
      "Episode:50/200, Reward:64.000\n",
      "Episode:60/200, Reward:90.000\n",
      "Episode:70/200, Reward:23.000\n",
      "Episode:80/200, Reward:138.000\n",
      "Episode:90/200, Reward:150.000\n",
      "Episode:100/200, Reward:200.000\n",
      "Episode:110/200, Reward:200.000\n",
      "Episode:120/200, Reward:200.000\n",
      "Episode:130/200, Reward:200.000\n",
      "Episode:140/200, Reward:200.000\n",
      "Episode:150/200, Reward:200.000\n",
      "Episode:160/200, Reward:200.000\n",
      "Episode:170/200, Reward:200.000\n",
      "Episode:180/200, Reward:200.000\n",
      "Episode:190/200, Reward:200.000\n",
      "Episode:200/200, Reward:200.000\n",
      "Complete training！\n",
      "results saved!\n"
     ]
    },
    {
     "output_type": "display_data",
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     },
     "metadata": {}
    },
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Start to eval !\n",
      "Env:CartPole-v0, Algorithm:PPO, Device:cuda\n",
      "Episode:10/200, Reward:200.000\n",
      "Episode:20/200, Reward:183.000\n",
      "Episode:30/200, Reward:157.000\n",
      "Episode:40/200, Reward:200.000\n",
      "Episode:50/200, Reward:113.000\n",
      "Complete evaling！\n",
      "results saved!\n"
     ]
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
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\n"
     },
     "metadata": {}
    }
   ],
   "source": [
    "if __name__ == '__main__':\n",
    "    cfg  = PPOConfig()\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)"
   ]
  }
 ]
}