152 lines
82 KiB
Plaintext
152 lines
82 KiB
Plaintext
{
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"metadata": {
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.7.10-final"
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},
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"orig_nbformat": 2,
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"kernelspec": {
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"name": "python3",
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"display_name": "Python 3.7.10 64-bit ('py37': conda)",
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"metadata": {
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"interpreter": {
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"hash": "fbea1422c2cf61ed9c0cfc03f38f71cc9083cc288606edc4170b5309b352ce27"
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}
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}
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2,
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {},
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"outputs": [],
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"source": [
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"import sys\n",
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"from pathlib import Path\n",
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"curr_path = str(Path().absolute())\n",
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"parent_path = str(Path().absolute().parent)\n",
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"sys.path.append(parent_path) # add current terminal path to sys.path\n",
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"\n",
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"import gym\n",
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"\n",
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"from envs.gridworld_env import CliffWalkingWapper, FrozenLakeWapper\n",
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"from QLearning.agent import QLearning\n",
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"from common.plot import plot_rewards\n",
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"from common.utils import save_results"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {},
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"outputs": [],
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"source": [
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"class QlearningConfig:\n",
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" '''训练相关参数'''\n",
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" def __init__(self):\n",
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" self.train_eps = 200 # 训练的episode数目\n",
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" self.gamma = 0.9 # reward的衰减率\n",
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" self.epsilon_start = 0.99 # e-greedy策略中初始epsilon\n",
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" self.epsilon_end = 0.01 # e-greedy策略中的终止epsilon\n",
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" self.epsilon_decay = 200 # e-greedy策略中epsilon的衰减率\n",
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" self.lr = 0.1 # learning rate"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {},
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"outputs": [],
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"source": [
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"def train(cfg,env,agent):\n",
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" rewards = [] \n",
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" ma_rewards = [] # moving average reward\n",
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" steps = [] # 记录所有episode的steps\n",
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" for i_episode in range(cfg.train_eps):\n",
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" ep_reward = 0 # 记录每个episode的reward\n",
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" ep_steps = 0 # 记录每个episode走了多少step\n",
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" state = env.reset() # 重置环境, 重新开一局（即开始新的一个episode）\n",
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" while True:\n",
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" action = agent.choose_action(state) # 根据算法选择一个动作\n",
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" next_state, reward, done, _ = env.step(action) # 与环境进行一次动作交互\n",
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" agent.update(state, action, reward, next_state, done) # Q-learning算法更新\n",
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" state = next_state # 存储上一个观察值\n",
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" ep_reward += reward\n",
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" ep_steps += 1 # 计算step数\n",
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" if done:\n",
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" break\n",
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" steps.append(ep_steps)\n",
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" rewards.append(ep_reward)\n",
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" if ma_rewards:\n",
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" ma_rewards.append(ma_rewards[-1]*0.9+ep_reward*0.1)\n",
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" else:\n",
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" ma_rewards.append(ep_reward)\n",
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" if (i_episode+1)%10==0:\n",
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" print(\"Episode:{}/{}: reward:{:.1f}\".format(i_episode+1, cfg.train_eps,ep_reward))\n",
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" return rewards,ma_rewards"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"metadata": {},
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"outputs": [
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{
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"output_type": "stream",
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"name": "stdout",
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"text": [
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"Episode:10/200: reward:-82.0\n",
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"Episode:20/200: reward:-59.0\n",
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"Episode:30/200: reward:-50.0\n",
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"Episode:40/200: reward:-32.0\n",
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"Episode:50/200: reward:-102.0\n",
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"Episode:60/200: reward:-151.0\n",
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"Episode:70/200: reward:-34.0\n",
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"Episode:80/200: reward:-71.0\n",
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"Episode:90/200: reward:-34.0\n",
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"Episode:100/200: reward:-26.0\n",
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"Episode:110/200: reward:-32.0\n",
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"Episode:120/200: reward:-48.0\n",
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"Episode:130/200: reward:-25.0\n",
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"Episode:140/200: reward:-31.0\n",
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"Episode:150/200: reward:-38.0\n",
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"Episode:160/200: reward:-47.0\n",
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"Episode:170/200: reward:-29.0\n",
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"Episode:180/200: reward:-36.0\n",
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"Episode:190/200: reward:-21.0\n",
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"Episode:200/200: reward:-34.0\n"
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]
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},
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{
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"output_type": "display_data",
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"data": {
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\n"
|
||
},
|
||
"metadata": {}
|
||
}
|
||
],
|
||
"source": [
|
||
"cfg = QlearningConfig()\n",
|
||
"env = gym.make(\"CliffWalking-v0\") # 0 up, 1 right, 2 down, 3 left\n",
|
||
"env = CliffWalkingWapper(env)\n",
|
||
"action_dim = env.action_space.n\n",
|
||
"agent = QLearning(action_dim,cfg)\n",
|
||
"rewards,ma_rewards = train(cfg,env,agent)\n",
|
||
"plot_rewards(rewards,ma_rewards,tag=\"train\",algo = \"On-Policy First-Visit MC Control\",save=False)"
|
||
]
|
||
}
|
||
]
|
||
} |