update projects

projects/codes/SoftActorCritic/task0.py

@@ -0,0 +1,142 @@
+#!/usr/bin/env python
+# coding=utf-8
+'''
+Author: JiangJi
+Email: johnjim0816@gmail.com
+Date: 2021-04-29 12:59:22
+LastEditor: JiangJi
+LastEditTime: 2021-12-22 16:27:13
+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 SoftActorCritic.env_wrapper import NormalizedActions
+from SoftActorCritic.sac import SAC
+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
+algo_name = 'SAC'  # 算法名称
+env_name = 'Pendulum-v1'  # 环境名称
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu") # 检测GPU
+
+class SACConfig:
+    def __init__(self) -> None:
+        self.algo_name = algo_name
+        self.env_name = env_name # 环境名称
+        self.device= device
+        self.train_eps = 300
+        self.test_eps = 20
+        self.max_steps = 500 # 每回合的最大步数
+        self.gamma = 0.99
+        self.mean_lambda=1e-3
+        self.std_lambda=1e-3
+        self.z_lambda=0.0
+        self.soft_tau=1e-2
+        self.value_lr  = 3e-4
+        self.soft_q_lr = 3e-4
+        self.policy_lr = 3e-4
+        self.capacity = 1000000
+        self.hidden_dim = 256
+        self.batch_size  = 128
+        
+
+class PlotConfig:
+    def __init__(self) -> None:
+        self.algo_name = algo_name  # 算法名称
+        self.env_name = env_name  # 环境名称
+        self.device= device
+        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 = NormalizedActions(gym.make(cfg.env_name))
+    env.seed(seed)
+    n_actions = env.action_space.shape[0]
+    n_states  = env.observation_space.shape[0]
+    agent = SAC(n_states,n_actions,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}')
+    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.train_eps):
+        ep_reward = 0 # 记录一回合内的奖励
+        state = env.reset() # 重置环境，返回初始状态
+        for i_step in range(cfg.max_steps):
+            action = agent.policy_net.get_action(state)
+            next_state, reward, done, _ = env.step(action)
+            agent.memory.push(state, action, reward, next_state, done)
+            agent.update()
+            state = next_state
+            ep_reward += reward
+            if done:
+                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) 
+        if (i_ep+1)%10 == 0: 
+            print(f'回合：{i_ep+1}/{cfg.train_eps}, 奖励：{ep_reward:.3f}')
+    print('完成训练！')
+    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 = [] # 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.test_eps):
+        state = env.reset()
+        ep_reward = 0
+        for i_step in range(cfg.max_steps):
+            action = agent.policy_net.get_action(state)
+            next_state, reward, done, _ = env.step(action)
+            state = next_state
+            ep_reward += reward
+            if done:
+                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(f"回合：{i_ep+1}/{cfg.test_eps}，奖励：{ep_reward:.1f}")
+    print('完成测试！')
+    return rewards, ma_rewards
+
+if __name__ == "__main__":
+    cfg=SACConfig()
+    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)  # 保存模型
+    save_results(rewards, ma_rewards, tag='train',
+                path=plot_cfg.result_path)  # 保存结果
+    plot_rewards(rewards, ma_rewards, plot_cfg, tag="train")  # 画出结果
+    # 测试
+    env, agent = env_agent_config(cfg, seed=10)
+    agent.load(path=plot_cfg.model_path)  # 导入模型
+    rewards, ma_rewards = test(cfg, env, agent)
+    save_results(rewards, ma_rewards, tag='test', path=plot_cfg.result_path)  # 保存结果
+    plot_rewards(rewards, ma_rewards, plot_cfg, tag="test")  # 画出结果
+
+
+
+