hot update PG

projects/codes/Sarsa/main.py

@@ -0,0 +1,136 @@
+#!/usr/bin/env python
+# coding=utf-8
+'''
+Author: John
+Email: johnjim0816@gmail.com
+Date: 2021-03-11 17:59:16
+LastEditor: John
+LastEditTime: 2022-08-25 14:26:36
+Discription: 
+Environment: 
+'''
+import sys,os
+os.environ["KMP_DUPLICATE_LIB_OK"]  =  "TRUE" # avoid "OMP: Error #15: Initializing libiomp5md.dll, but found libiomp5md.dll already initialized."
+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 path to system path
+import gym
+import datetime
+import argparse
+from envs.register import register_env
+from envs.wrappers import CliffWalkingWapper
+from Sarsa.sarsa import Sarsa
+from common.utils import save_results,make_dir,plot_rewards,save_args,all_seed
+
+def get_args():
+    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='Sarsa',type=str,help="name of algorithm")
+    parser.add_argument('--env_name',default='Racetrack-v0',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('--epsilon_start',default=0.90,type=float,help="initial value of epsilon") 
+    parser.add_argument('--epsilon_end',default=0.01,type=float,help="final value of epsilon") 
+    parser.add_argument('--epsilon_decay',default=200,type=int,help="decay rate of epsilon") 
+    parser.add_argument('--lr',default=0.2,type=float,help="learning rate")
+    parser.add_argument('--device',default='cpu',type=str,help="cpu or cuda") 
+    parser.add_argument('--seed',default=10,type=int,help="seed") 
+    parser.add_argument('--show_fig',default=False,type=bool,help="if show figure or not")  
+    parser.add_argument('--save_fig',default=True,type=bool,help="if save figure or not")   
+    args = parser.parse_args()   
+    default_args = {'result_path':f"{curr_path}/outputs/{args.env_name}/{curr_time}/results/",
+                    'model_path':f"{curr_path}/outputs/{args.env_name}/{curr_time}/models/",
+    }
+    args = {**vars(args),**default_args}  # type(dict)                         
+    return args
+
+def env_agent_config(cfg):
+    register_env(cfg['env_name'])
+    env = gym.make(cfg['env_name'])
+    if cfg['seed'] !=0: # set random seed
+        all_seed(env,seed= cfg['seed']) 
+    if cfg['env_name'] == 'CliffWalking-v0':
+        env = CliffWalkingWapper(env)
+    try: # state dimension
+        n_states = env.observation_space.n # print(hasattr(env.observation_space, 'n'))
+    except AttributeError:
+        n_states = env.observation_space.shape[0] # print(hasattr(env.observation_space, 'shape'))
+    n_actions = env.action_space.n  # action dimension
+    print(f"n_states: {n_states}, n_actions: {n_actions}")
+    cfg.update({"n_states":n_states,"n_actions":n_actions}) # update to cfg paramters
+    agent = Sarsa(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']}")
+    rewards = []  # record rewards for all episodes
+    steps = [] # record steps for all episodes
+    for i_ep in range(cfg['train_eps']):
+        ep_reward = 0  # reward per episode
+        ep_step = 0 # step per episode
+        state = env.reset()  # reset and obtain initial state
+        action = agent.sample_action(state)
+        while True:
+        # for _ in range(cfg.ep_max_steps):
+            next_state, reward, done, _ = env.step(action)  # update env and return transitions
+            next_action =  agent.sample_action(next_state)
+            agent.update(state, action, reward, next_state, next_action,done)  # update agent
+            state = next_state  # update state
+            action = next_action
+            ep_reward += reward
+            ep_step += 1
+            if done:
+                break
+        rewards.append(ep_reward)
+        steps.append(ep_step)
+        if (i_ep+1)%10==0:
+            print(f'Episode: {i_ep+1}/{cfg["train_eps"]}, Reward: {ep_reward:.2f}, Steps:{ep_step}, Epislon: {agent.epsilon:.3f}')
+    print("Finish training!")
+    return {'episodes':range(len(rewards)),'rewards':rewards,'steps':steps}
+
+def test(cfg,env,agent):
+    print("Start testing!")
+    print(f"Env: {cfg['env_name']}, Algorithm: {cfg['algo_name']}, Device: {cfg['device']}")
+    rewards = []  # record rewards for all episodes
+    steps = [] # record steps for all episodes
+    for i_ep in range(cfg['test_eps']):
+        ep_reward = 0  # reward per episode
+        ep_step = 0
+        while True:
+        # for _ in range(cfg.ep_max_steps):
+            action = agent.predict_action(state)
+            next_state, reward, done = env.step(action)
+            state = next_state
+            ep_reward+=reward
+            ep_step+=1
+            if done:
+                break  
+        rewards.append(ep_reward)
+        steps.append(ep_step)
+        print(f"Episode: {i_ep+1}/{cfg['test_eps']}, Steps:{ep_step}, Reward: {ep_reward:.2f}")
+    print("Finish testing!")
+    return {'episodes':range(len(rewards)),'rewards':rewards,'steps':steps}
+
+if __name__ == "__main__":
+    cfg = get_args()
+    # 训练
+    env, agent = env_agent_config(cfg)
+    res_dic = train(cfg, env, agent)
+    make_dir(cfg.result_path, cfg.model_path)  
+    save_args(cfg) # save parameters
+    agent.save(path=cfg.model_path)  # save model
+    save_results(res_dic, tag='train',
+                 path=cfg.result_path)  
+    plot_rewards(res_dic['rewards'], cfg, tag="train")  
+    # 测试
+    env, agent = env_agent_config(cfg)
+    agent.load(path=cfg.model_path)  # 导入模型
+    res_dic = test(cfg, env, agent)
+    save_results(res_dic, tag='test',
+                 path=cfg.result_path)  # 保存结果
+    plot_rewards(res_dic['rewards'], cfg, tag="test")  # 画出结果
+    
+    
+

projects/codes/Sarsa/sarsa.py

@@ -5,7 +5,7 @@ Author: John
 Email: johnjim0816@gmail.com
 Date: 2021-03-12 16:58:16
 LastEditor: John
-LastEditTime: 2022-08-04 22:22:16
+LastEditTime: 2022-08-25 00:23:22
 Discription: 
 Environment: 
 '''
@@ -14,45 +14,51 @@ from collections import defaultdict
 import torch
 import math
 class Sarsa(object):
-    def __init__(self,
-                 n_actions,cfg):
-        self.n_actions = n_actions  
-        self.lr = cfg.lr  
-        self.gamma = cfg.gamma  
-        self.sample_count = 0 
-        self.epsilon_start = cfg.epsilon_start
-        self.epsilon_end = cfg.epsilon_end
-        self.epsilon_decay = cfg.epsilon_decay 
-        self.Q  = defaultdict(lambda: np.zeros(n_actions)) # Q table
-    def sample(self, state):
+    def __init__(self,cfg):
+        self.n_actions = cfg['n_actions'] 
+        self.lr = cfg['lr']  
+        self.gamma = cfg['gamma']  
+        self.epsilon = cfg['epsilon_start']
+        self.sample_count = 0  
+        self.epsilon_start = cfg['epsilon_start']
+        self.epsilon_end = cfg['epsilon_end']
+        self.epsilon_decay = cfg['epsilon_decay']
+        self.Q_table  = defaultdict(lambda: np.zeros(self.n_actions)) # Q table
+    def sample_action(self, state):
+        ''' another way to represent e-greedy policy
+        '''
         self.sample_count += 1
         self.epsilon = self.epsilon_end + (self.epsilon_start - self.epsilon_end) * \
             math.exp(-1. * self.sample_count / self.epsilon_decay) # The probability to select a random action, is is log decayed
-        best_action = np.argmax(self.Q[state])
+        best_action = np.argmax(self.Q_table[state])
         action_probs = np.ones(self.n_actions, dtype=float) * self.epsilon / self.n_actions
         action_probs[best_action] += (1.0 - self.epsilon)
         action = np.random.choice(np.arange(len(action_probs)), p=action_probs) 
         return action
-    def predict(self,state):
-        return np.argmax(self.Q[state])
-    def update(self, state, action, reward, next_state, next_action,done):
-        Q_predict = self.Q[state][action]
-        if done:
-            Q_target = reward  # 终止状态
-        else:
-            Q_target = reward + self.gamma * self.Q[next_state][next_action] # 与Q learning不同，Sarsa是拿下一步动作对应的Q值去更新
-        self.Q[state][action] += self.lr * (Q_target - Q_predict) 
-    def save(self,path):
-        '''把 Q表格 的数据保存到文件中
+    def predict_action(self,state):
+        ''' predict action while testing 
         '''
+        action = np.argmax(self.Q_table[state])
+        return action
+    def update(self, state, action, reward, next_state, next_action,done):
+        Q_predict = self.Q_table[state][action]
+        if done:
+            Q_target = reward  # terminal state
+        else:
+            Q_target = reward + self.gamma * self.Q_table[next_state][next_action] # the only difference from Q learning
+        self.Q_table[state][action] += self.lr * (Q_target - Q_predict) 
+    def save_model(self,path):
         import dill
+        from pathlib import Path
+        # create path
+        Path(path).mkdir(parents=True, exist_ok=True)
         torch.save(
-            obj=self.Q,
-            f=path+"sarsa_model.pkl",
+            obj=self.Q_table_table,
+            f=path+"checkpoint.pkl",
             pickle_module=dill
         )
-    def load(self, path):
-        '''从文件中读取数据到 Q表格
-        '''
+        print("Model saved!")
+    def load_model(self, path):
         import dill
-        self.Q =torch.load(f=path+'sarsa_model.pkl',pickle_module=dill)
+        self.Q_table_table =torch.load(f=path+'checkpoint.pkl',pickle_module=dill)
+        print("Mode loaded!")

projects/codes/Sarsa/task0.py

@@ -1,118 +0,0 @@
-#!/usr/bin/env python
-# coding=utf-8
-'''
-Author: John
-Email: johnjim0816@gmail.com
-Date: 2021-03-11 17:59:16
-LastEditor: John
-LastEditTime: 2022-08-04 22:28:51
-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 datetime
-import argparse
-from envs.racetrack_env import RacetrackEnv
-from Sarsa.sarsa import Sarsa
-from common.utils import save_results,make_dir,plot_rewards,save_args
-
-def get_args():
-    """ 超参数
-    """
-    curr_time = datetime.datetime.now().strftime("%Y%m%d-%H%M%S")  # 获取当前时间
-    parser = argparse.ArgumentParser(description="hyperparameters")      
-    parser.add_argument('--algo_name',default='Sarsa',type=str,help="name of algorithm")
-    parser.add_argument('--env_name',default='CliffWalking-v0',type=str,help="name of environment")
-    parser.add_argument('--train_eps',default=300,type=int,help="episodes of training") # 训练的回合数
-    parser.add_argument('--test_eps',default=20,type=int,help="episodes of testing") # 测试的回合数
-    parser.add_argument('--ep_max_steps',default=200,type=int) # 每回合最大的部署
-    parser.add_argument('--gamma',default=0.99,type=float,help="discounted factor") # 折扣因子
-    parser.add_argument('--epsilon_start',default=0.90,type=float,help="initial value of epsilon") #  e-greedy策略中初始epsilon
-    parser.add_argument('--epsilon_end',default=0.01,type=float,help="final value of epsilon") # e-greedy策略中的终止epsilon
-    parser.add_argument('--epsilon_decay',default=200,type=int,help="decay rate of epsilon") # e-greedy策略中epsilon的衰减率
-    parser.add_argument('--lr',default=0.2,type=float,help="learning rate")
-    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 = RacetrackEnv()
-    n_actions = 9 # 动作数
-    agent = Sarsa(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 = []  # 记录奖励
-    for i_ep in range(cfg.train_eps):
-        state = env.reset()
-        action = agent.sample(state)
-        ep_reward = 0
-        # while True:
-        for _ in range(cfg.ep_max_steps):
-            next_state, reward, done = env.step(action)
-            ep_reward+=reward
-            next_action = agent.sample(next_state)
-            agent.update(state, action, reward, next_state, next_action,done)
-            state = next_state
-            action = next_action
-            if done:
-                break  
-        rewards.append(ep_reward)
-        if (i_ep+1)%2==0:
-            print(f"回合：{i_ep+1}/{cfg.train_eps}，奖励：{ep_reward:.1f}，Epsilon：{agent.epsilon}")
-    print('完成训练！')
-    return {"rewards":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 = []
-    for i_ep in range(cfg.test_eps):
-        state = env.reset()
-        ep_reward = 0
-        # while True:
-        for _ in range(cfg.ep_max_steps):
-            action = agent.predict(state)
-            next_state, reward, done = env.step(action)
-            ep_reward+=reward
-            state = next_state
-            if done:
-                break  
-        rewards.append(ep_reward)
-        print(f"回合数：{i_ep+1}/{cfg.test_eps}, 奖励：{ep_reward:.1f}")
-    print('完成测试！')
-    return {"rewards":rewards}
-        
-if __name__ == "__main__":
-    cfg = get_args()
-    # 训练
-    env, agent = env_agent_config(cfg)
-    res_dic = train(cfg, env, agent)
-    make_dir(cfg.result_path, cfg.model_path)  
-    save_args(cfg) # save parameters
-    agent.save(path=cfg.model_path)  # save model
-    save_results(res_dic, tag='train',
-                 path=cfg.result_path)  
-    plot_rewards(res_dic['rewards'], cfg, tag="train")  
-    # 测试
-    env, agent = env_agent_config(cfg)
-    agent.load(path=cfg.model_path)  # 导入模型
-    res_dic = test(cfg, env, agent)
-    save_results(res_dic, tag='test',
-                 path=cfg.result_path)  # 保存结果
-    plot_rewards(res_dic['rewards'], cfg, tag="test")  # 画出结果
-    
-    
-