update projects

projects/codes/DDPG/task0.py

@@ -0,0 +1,133 @@
+#!/usr/bin/env python
+# coding=utf-8
+'''
+@Author: John
+@Email: johnjim0816@gmail.com
+@Date: 2020-06-11 20:58:21
+@LastEditor: John
+LastEditTime: 2022-07-21 21:51:34
+@Discription: 
+@Environment: python 3.7.7
+'''
+import sys,os
+curr_path = os.path.dirname(os.path.abspath(__file__))  # current path
+parent_path = os.path.dirname(curr_path)  # parent path
+sys.path.append(parent_path)  # add to system path
+
+import datetime
+import gym
+import torch
+import argparse
+
+from env import NormalizedActions,OUNoise
+from ddpg import DDPG
+from common.utils import save_results,make_dir
+from common.utils import plot_rewards,save_args
+
+def get_args():
+    """ Hyperparameters
+    """
+    curr_time = datetime.datetime.now().strftime("%Y%m%d-%H%M%S")  # Obtain current time
+    parser = argparse.ArgumentParser(description="hyperparameters")      
+    parser.add_argument('--algo_name',default='DDPG',type=str,help="name of algorithm")
+    parser.add_argument('--env_name',default='Pendulum-v1',type=str,help="name of environment")
+    parser.add_argument('--train_eps',default=300,type=int,help="episodes of training")
+    parser.add_argument('--test_eps',default=20,type=int,help="episodes of testing")
+    parser.add_argument('--gamma',default=0.99,type=float,help="discounted factor")
+    parser.add_argument('--critic_lr',default=1e-3,type=float,help="learning rate of critic")
+    parser.add_argument('--actor_lr',default=1e-4,type=float,help="learning rate of actor")
+    parser.add_argument('--memory_capacity',default=8000,type=int,help="memory capacity")
+    parser.add_argument('--batch_size',default=128,type=int)
+    parser.add_argument('--target_update',default=2,type=int)
+    parser.add_argument('--soft_tau',default=1e-2,type=float)
+    parser.add_argument('--hidden_dim',default=256,type=int)
+    parser.add_argument('--device',default='cpu',type=str,help="cpu or cuda") 
+    parser.add_argument('--result_path',default=curr_path + "/outputs/" + parser.parse_args().env_name + \
+            '/' + curr_time + '/results/' )
+    parser.add_argument('--model_path',default=curr_path + "/outputs/" + parser.parse_args().env_name + \
+            '/' + curr_time + '/models/' ) # path to save models
+    parser.add_argument('--save_fig',default=True,type=bool,help="if save figure or not")   
+    args = parser.parse_args()                           
+    return args
+
+def env_agent_config(cfg,seed=1):
+    env = NormalizedActions(gym.make(cfg.env_name)) # 装饰action噪声
+    env.seed(seed) # 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)
+    return env,agent
+def train(cfg, env, agent):
+    print('Start training!')
+    print(f'Env:{cfg.env_name}, Algorithm:{cfg.algo_name}, Device:{cfg.device}')
+    ou_noise = OUNoise(env.action_space)  # noise of action
+    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):
+        state = env.reset()
+        ou_noise.reset()
+        done = False
+        ep_reward = 0
+        i_step = 0
+        while not done:
+            i_step += 1
+            action = agent.choose_action(state)
+            action = ou_noise.get_action(action, i_step) 
+            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 (i_ep+1)%10 == 0:
+            print(f'Env:{i_ep+1}/{cfg.train_eps}, Reward:{ep_reward:.2f}')
+        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('Finish training!')
+    return {'rewards':rewards,'ma_rewards':ma_rewards}
+
+def test(cfg, env, agent):
+    print('Start testing')
+    print(f'Env:{cfg.env_name}, Algorithm:{cfg.algo_name}, Device:{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() 
+        done = False
+        ep_reward = 0
+        i_step = 0
+        while not done:
+            i_step += 1
+            action = agent.choose_action(state)
+            next_state, reward, done, _ = env.step(action)
+            ep_reward += reward
+            state = next_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(f"Epside:{i_ep+1}/{cfg.test_eps}, Reward:{ep_reward:.1f}")
+    print('Finish testing!')
+    return {'rewards':rewards,'ma_rewards':ma_rewards}
+if __name__ == "__main__":
+    cfg = get_args()
+    # training
+    env,agent = env_agent_config(cfg,seed=1)
+    res_dic = train(cfg, env, agent)
+    make_dir(cfg.result_path, cfg.model_path)
+    save_args(cfg)
+    agent.save(path=cfg.model_path)
+    save_results(res_dic, tag='train',
+                 path=cfg.result_path)  
+    plot_rewards(res_dic['rewards'], res_dic['ma_rewards'], cfg, tag="train")  
+    # testing
+    env,agent = env_agent_config(cfg,seed=10)
+    agent.load(path=cfg.model_path)
+    res_dic = test(cfg,env,agent)
+    save_results(res_dic, tag='test',
+                 path=cfg.result_path)  
+    plot_rewards(res_dic['rewards'], res_dic['ma_rewards'], cfg, tag="test")   
+