update codes

codes/PPO/task1_train.py

@@ -0,0 +1,132 @@
+#!/usr/bin/env python
+# coding=utf-8
+'''
+Author: John
+Email: johnjim0816@gmail.com
+Date: 2021-03-22 16:18:10
+LastEditor: John
+LastEditTime: 2021-09-26 22:05:00
+Discription: 
+Environment: 
+'''
+import sys,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 PPO.agent import PPO
+from common.plot import plot_rewards
+from common.utils import save_results,make_dir
+
+curr_time = datetime.datetime.now().strftime("%Y%m%d-%H%M%S") # obtain current time
+
+class PPOConfig:
+    def __init__(self) -> None:
+        self.algo = "PPO"  # 算法名称
+        self.env_name = 'Pendulum-v1' # 环境名称
+        self.continuous = True # 环境是否为连续动作
+        self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")  # 检测GPU
+        self.train_eps = 200 # 训练的回合数
+        self.eval_eps = 20 # 测试的回合数
+        self.batch_size = 5
+        self.gamma=0.99
+        self.n_epochs = 4
+        self.actor_lr = 0.0003
+        self.critic_lr = 0.0003
+        self.gae_lambda=0.95
+        self.policy_clip=0.2
+        self.hidden_dim = 256
+        self.update_fre = 20 # frequency of agent update
+
+class PlotConfig:
+    def __init__(self) -> None:
+        self.algo = "PPO"  # 算法名称
+        self.env_name = 'Pendulum-v1' # 环境名称
+        self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")  # 检测GPU
+        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)
+    state_dim = env.observation_space.shape[0]
+    action_dim = env.action_space.shape[0]
+    agent = PPO(state_dim,action_dim,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}, 设备：{cfg.device}')
+    rewards = [] # 记录所有回合的奖励
+    ma_rewards = []  # 记录所有回合的滑动平均奖励
+    steps = 0
+    for i_ep in range(cfg.train_eps):
+        state = env.reset()
+        done = False
+        ep_reward = 0
+        while not done:
+            action, prob, val = agent.choose_action(state,continuous=cfg.continuous)
+            print(action)
+            state_, reward, done, _ = env.step(action)
+            steps += 1
+            ep_reward += reward
+            agent.memory.push(state, action, prob, val, reward, done)
+            if steps % cfg.update_fre == 0:
+                agent.update()
+            state = state_
+        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)
+        if (i_ep+1)%10 == 0: 
+            print(f"回合：{i_ep+1}/{cfg.train_eps}，奖励：{ep_reward:.2f}")
+    print('完成训练！')
+    return rewards,ma_rewards
+
+def eval(cfg,env,agent):
+    print('开始测试!')
+    print(f'环境：{cfg.env_name}, 算法：{cfg.algo}, 设备：{cfg.device}')
+    rewards = [] # 记录所有回合的奖励
+    ma_rewards = []  # 记录所有回合的滑动平均奖励
+    for i_ep in range(cfg.eval_eps):
+        state = env.reset()
+        done = False
+        ep_reward = 0
+        while not done:
+            action, prob, val = agent.choose_action(state,continuous=False)
+            state_, reward, done, _ = env.step(action)
+            ep_reward += reward
+            state = state_
+        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('回合：{}/{}, 奖励：{}'.format(i_ep+1, cfg.eval_eps, ep_reward))
+    print('完成训练！')
+    return rewards,ma_rewards
+    
+if __name__ == '__main__':
+    cfg  = PPOConfig()
+    plot_cfg = PlotConfig()
+    # 训练
+    env,agent = env_agent_config(cfg,seed=1)
+    rewards, ma_rewards = train(cfg, env, agent)
+    make_dir(plot_cfg.result_path, plot_cfg.model_path) # 创建保存结果和模型路径的文件夹
+    agent.save(path=plot_cfg.model_path)
+    save_results(rewards, ma_rewards, tag='train', path=plot_cfg.result_path)
+    plot_rewards(rewards, ma_rewards, plot_cfg, tag="train")
+    # 测试
+    env,agent = env_agent_config(cfg,seed=10)
+    agent.load(path=plot_cfg.model_path)
+    rewards,ma_rewards = eval(cfg,env,agent)
+    save_results(rewards,ma_rewards,tag='eval',path=plot_cfg.result_path)
+    plot_rewards(rewards,ma_rewards,plot_cfg,tag="eval")