add DDPG

2021-03-23 17:32:39 +08:00
parent 9d244ee933
commit 1ab35d985e
5 changed files with 251 additions and 0 deletions
--- a/codes/DDPG/main.py
+++ b/codes/DDPG/main.py
@@ -0,0 +1,92 @@
+#!/usr/bin/env python
+# coding=utf-8
+'''
+@Author: John
+@Email: johnjim0816@gmail.com
+@Date: 2020-06-11 20:58:21
+@LastEditor: John
+LastEditTime: 2021-03-19 19:57:00
+@Discription: 
+@Environment: python 3.7.7
+'''
+import sys,os
+sys.path.append(os.getcwd()) # 添加当前终端路径
+import torch
+import gym
+import numpy as np
+import datetime
+from DDPG.agent import DDPG
+from DDPG.env import NormalizedActions,OUNoise
+from common.plot import plot_rewards
+from common.utils import save_results
+
+SEQUENCE = datetime.datetime.now().strftime("%Y%m%d-%H%M%S") # 获取当前时间
+SAVED_MODEL_PATH = os.path.split(os.path.abspath(__file__))[0]+"/saved_model/"+SEQUENCE+'/' # 生成保存的模型路径
+if not os.path.exists(os.path.split(os.path.abspath(__file__))[0]+"/saved_model/"): # 检测是否存在文件夹
+    os.mkdir(os.path.split(os.path.abspath(__file__))[0]+"/saved_model/")
+if not os.path.exists(SAVED_MODEL_PATH): # 检测是否存在文件夹
+    os.mkdir(SAVED_MODEL_PATH)
+RESULT_PATH = os.path.split(os.path.abspath(__file__))[0]+"/results/"+SEQUENCE+'/' # 存储reward的路径
+if not os.path.exists(os.path.split(os.path.abspath(__file__))[0]+"/results/"): # 检测是否存在文件夹
+    os.mkdir(os.path.split(os.path.abspath(__file__))[0]+"/results/")
+if not os.path.exists(RESULT_PATH): # 检测是否存在文件夹
+    os.mkdir(RESULT_PATH)
+
+class DDPGConfig:
+    def __init__(self):
+        self.gamma = 0.99
+        self.critic_lr = 1e-3  
+        self.actor_lr = 1e-4 
+        self.memory_capacity = 10000
+        self.batch_size = 128
+        self.train_eps =300
+        self.train_steps = 200
+        self.eval_eps = 200
+        self.eval_steps = 200
+        self.target_update = 4
+        self.hidden_dim = 30
+        self.soft_tau=1e-2
+        self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+def train(cfg,env,agent):
+    print('Start to train ! ')
+    ou_noise = OUNoise(env.action_space) # action noise
+    rewards = []
+    ma_rewards = [] # moving average rewards
+    ep_steps = []
+    for i_episode in range(cfg.train_eps):
+        state = env.reset()
+        ou_noise.reset()
+        ep_reward = 0
+        for i_step in range(cfg.train_steps):
+            action = agent.choose_action(state)
+            action = ou_noise.get_action(
+                action, i_step)  # 即paper中的random process
+            next_state, reward, done, _ = env.step(action)
+            ep_reward += reward
+            agent.memory.push(state, action, reward, next_state, done)
+            agent.update()
+            state = next_state
+            if done:
+                break
+        print('Episode:{}/{}, Reward:{}, Steps:{}, Done:{}'.format(i_episode+1,cfg.train_eps,ep_reward,i_step+1,done))
+        ep_steps.append(i_step)
+        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('Complete training！')
+    return rewards,ma_rewards
+
+if __name__ == "__main__":
+    cfg = DDPGConfig()
+    env = NormalizedActions(gym.make("Pendulum-v0"))
+    env.seed(1) # 设置env随机种子
+    n_states = env.observation_space.shape[0]
+    n_actions = env.action_space.shape[0]
+    agent = DDPG(n_states,n_actions,cfg)
+    rewards,ma_rewards = train(cfg,env,agent)
+    agent.save(path=SAVED_MODEL_PATH)
+    save_results(rewards,ma_rewards,tag='train',path=RESULT_PATH)
+    plot_rewards(rewards,ma_rewards,tag="train",algo = cfg.algo,path=RESULT_PATH)
+