update

2021-05-06 02:07:56 +08:00
parent 747f3238c0
commit b17c8f4e41
107 changed files with 1439 additions and 987 deletions
--- a/codes/PPO/task0_train.py
+++ b/codes/PPO/task0_train.py
@@ -0,0 +1,121 @@
+#!/usr/bin/env python
+# coding=utf-8
+'''
+Author: John
+Email: johnjim0816@gmail.com
+Date: 2021-03-22 16:18:10
+LastEditor: John
+LastEditTime: 2021-05-06 00:43:36
+Discription: 
+Environment: 
+'''
+import sys,os
+curr_path = os.path.dirname(__file__)
+parent_path=os.path.dirname(curr_path) 
+sys.path.append(parent_path) # add current terminal path to sys.path
+
+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.env = 'CartPole-v0'
+        self.algo = 'PPO'
+        self.result_path = curr_path+"/results/" +self.env+'/'+curr_time+'/results/'  # path to save results
+        self.model_path = curr_path+"/results/" +self.env+'/'+curr_time+'/models/'  # path to save models
+        self.train_eps = 200 # max training episodes
+        self.eval_eps = 50
+        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
+        self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu") # check gpu
+
+def env_agent_config(cfg,seed=1):
+    env = gym.make(cfg.env)  
+    env.seed(seed)
+    state_dim = env.observation_space.shape[0]
+    action_dim = env.action_space.n
+    agent = PPO(state_dim,action_dim,cfg)
+    return env,agent
+            
+def train(cfg,env,agent):
+    print('Start to train !')
+    print(f'Env:{cfg.env}, Algorithm:{cfg.algo}, Device:{cfg.device}')
+    rewards= []
+    ma_rewards = [] # moving average rewards
+    running_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)
+            state_, reward, done, _ = env.step(action)
+            running_steps += 1
+            ep_reward += reward
+            agent.memory.push(state, action, prob, val, reward, done)
+            if running_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)
+        print(f"Episode:{i_ep+1}/{cfg.train_eps}, Reward:{ep_reward:.3f}")
+    print('Complete training！')
+    return rewards,ma_rewards
+
+def eval(cfg,env,agent):
+    print('Start to eval !')
+    print(f'Env:{cfg.env}, Algorithm:{cfg.algo}, Device:{cfg.device}')
+    rewards= []
+    ma_rewards = [] # moving average rewards
+    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)
+            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(f"Episode:{i_ep+1}/{cfg.train_eps}, Reward:{ep_reward:.3f}")
+    print('Complete evaling！')
+    return rewards,ma_rewards
+    
+if __name__ == '__main__':
+    cfg  = PPOConfig()
+    # train
+    env,agent = env_agent_config(cfg,seed=1)
+    rewards, ma_rewards = train(cfg, env, agent)
+    make_dir(cfg.result_path, cfg.model_path)
+    agent.save(path=cfg.model_path)
+    save_results(rewards, ma_rewards, tag='train', path=cfg.result_path)
+    plot_rewards(rewards, ma_rewards, tag="train",
+                 algo=cfg.algo, path=cfg.result_path)
+    # eval
+    env,agent = env_agent_config(cfg,seed=10)
+    agent.load(path=cfg.model_path)
+    rewards,ma_rewards = eval(cfg,env,agent)
+    save_results(rewards,ma_rewards,tag='eval',path=cfg.result_path)
+    plot_rewards(rewards,ma_rewards,tag="eval",env=cfg.env,algo = cfg.algo,path=cfg.result_path)