update rainbowdqn

2022-05-31 01:20:58 +08:00
parent cfc0f6492e
commit c7c94468c9
149 changed files with 1866 additions and 1549 deletions
--- a/codes/PolicyGradient/task0.py
+++ b/codes/PolicyGradient/task0.py
@@ -0,0 +1,152 @@
+#!/usr/bin/env python
+# coding=utf-8
+'''
+Author: John
+Email: johnjim0816@gmail.com
+Date: 2020-11-22 23:21:53
+LastEditor: John
+LastEditTime: 2022-02-10 06:13:21
+Discription: 
+Environment: 
+'''
+import sys
+import os
+curr_path = os.path.dirname(os.path.abspath(__file__))  # 当前文件所在绝对路径
+parent_path = os.path.dirname(curr_path)  # 父路径
+sys.path.append(parent_path)  # 添加路径到系统路径
+
+import gym
+import torch
+import datetime
+from itertools import count
+
+from pg import PolicyGradient
+from common.utils import save_results, make_dir
+from common.utils import plot_rewards
+
+curr_time = datetime.datetime.now().strftime("%Y%m%d-%H%M%S")  # 获取当前时间
+
+class Config:
+    '''超参数
+    '''
+
+    def __init__(self):
+        ################################## 环境超参数 ###################################
+        self.algo_name = "PolicyGradient"  # 算法名称
+        self.env_name = 'CartPole-v0' # 环境名称
+        self.device = torch.device(
+            "cuda" if torch.cuda.is_available() else "cpu")  # 检测GPUgjgjlkhfsf风刀霜的撒发十
+        self.seed = 10 # 随机种子，置0则不设置随机种子
+        self.train_eps = 300 # 训练的回合数
+        self.test_eps = 30 # 测试的回合数
+        ################################################################################
+        
+        ################################## 算法超参数 ###################################
+        self.batch_size = 8 # mini-batch SGD中的批量大小
+        self.lr = 0.01 # 学习率
+        self.gamma = 0.99 # 强化学习中的折扣因子
+        self.hidden_dim = 36 # 网络隐藏层
+        ################################################################################
+        
+        ################################# 保存结果相关参数 ################################
+        self.result_path = curr_path + "/outputs/" + self.env_name + \
+            '/' + curr_time + '/results/'  # 保存结果的路径
+        self.model_path = curr_path + "/outputs/" + self.env_name + \
+            '/' + curr_time + '/models/'  # 保存模型的路径
+        self.save = True # 是否保存图片
+        ################################################################################
+
+
+def env_agent_config(cfg,seed=1):
+    env = gym.make(cfg.env_name)  
+    env.seed(seed)
+    n_states = env.observation_space.shape[0]
+    agent = PolicyGradient(n_states,cfg)
+    return env,agent
+
+def train(cfg,env,agent):
+    print('开始训练!')
+    print(f'环境：{cfg.env_name}, 算法：{cfg.algo_name}, 设备：{cfg.device}')
+    state_pool = [] # 存放每batch_size个episode的state序列
+    action_pool = []
+    reward_pool = [] 
+    rewards = []
+    ma_rewards = []
+    for i_ep in range(cfg.train_eps):
+        state = env.reset()
+        ep_reward = 0
+        for _ in count():
+            action = agent.choose_action(state) # 根据当前环境state选择action
+            next_state, reward, done, _ = env.step(action)
+            ep_reward += reward
+            if done:
+                reward = 0
+            state_pool.append(state)
+            action_pool.append(float(action))
+            reward_pool.append(reward)
+            state = next_state
+            if done:
+                print('回合：{}/{}, 奖励：{}'.format(i_ep + 1, cfg.train_eps, ep_reward))
+                break
+        if i_ep > 0 and i_ep % cfg.batch_size == 0:
+            agent.update(reward_pool,state_pool,action_pool)
+            state_pool = [] # 每个episode的state
+            action_pool = []
+            reward_pool = []
+        rewards.append(ep_reward)
+        if ma_rewards:
+            ma_rewards.append(
+                0.9*ma_rewards[-1]+0.1*ep_reward)
+        else:
+            ma_rewards.append(ep_reward)
+    print('完成训练！')
+    env.close()
+    return rewards, ma_rewards
+            
+
+def test(cfg,env,agent):
+    print('开始测试!')
+    print(f'环境：{cfg.env_name}, 算法：{cfg.algo_name}, 设备：{cfg.device}')
+    rewards = []
+    ma_rewards = []
+    for i_ep in range(cfg.test_eps):
+        state = env.reset()
+        ep_reward = 0
+        for _ in count():
+            action = agent.choose_action(state) # 根据当前环境state选择action
+            next_state, reward, done, _ = env.step(action)
+            ep_reward += reward
+            if done:
+                reward = 0
+            state = next_state
+            if done:
+                print('回合：{}/{}, 奖励：{}'.format(i_ep + 1, cfg.train_eps, ep_reward))
+                break
+        rewards.append(ep_reward)
+        if ma_rewards:
+            ma_rewards.append(
+                0.9*ma_rewards[-1]+0.1*ep_reward)
+        else:
+            ma_rewards.append(ep_reward)
+    print('完成测试！')
+    env.close()
+    return rewards, ma_rewards
+    
+if __name__ == "__main__":
+    cfg = Config()
+    # 训练
+    env, agent = env_agent_config(cfg)
+    rewards, ma_rewards = train(cfg, env, agent)
+    make_dir(cfg.result_path, cfg.model_path)  # 创建保存结果和模型路径的文件夹
+    agent.save(path=cfg.model_path)  # 保存模型
+    save_results(rewards, ma_rewards, tag='train',
+                 path=cfg.result_path)  # 保存结果
+    plot_rewards(rewards, ma_rewards, cfg, tag="train")  # 画出结果
+    # 测试
+    env, agent = env_agent_config(cfg)
+    agent.load(path=cfg.model_path)  # 导入模型
+    rewards, ma_rewards = test(cfg, env, agent)
+    save_results(rewards, ma_rewards, tag='test',
+                 path=cfg.result_path)  # 保存结果
+    plot_rewards(rewards, ma_rewards, cfg, tag="test")  # 画出结果
+