hot update PG

projects/codes/PolicyGradient/main.py

@@ -0,0 +1,129 @@
+#!/usr/bin/env python
+# coding=utf-8
+'''
+Author: John
+Email: johnjim0816@gmail.com
+Date: 2020-11-22 23:21:53
+LastEditor: John
+LastEditTime: 2022-08-25 20:59:23
+Discription: 
+Environment: 
+'''
+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 gym
+import torch
+import datetime
+import argparse
+from itertools import count
+import torch.nn.functional as F
+from pg import PolicyGradient
+from common.utils import save_results, make_dir,all_seed,save_args,plot_rewards
+from common.models import MLP
+from common.memories import PGReplay
+from common.launcher import Launcher
+from envs.register import register_env
+
+
+class PGNet(MLP):
+    ''' instead of outputing action, PG Net outputs propabilities of actions, we can use class inheritance from MLP here
+    '''
+    def forward(self, x):
+        x = F.relu(self.fc1(x))
+        x = F.relu(self.fc2(x))
+        x = F.sigmoid(self.fc3(x))
+        return x
+
+class Main(Launcher):
+    def get_args(self):
+        """ 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='PolicyGradient',type=str,help="name of algorithm")
+        parser.add_argument('--env_name',default='CartPole-v0',type=str,help="name of environment")
+        parser.add_argument('--train_eps',default=200,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('--lr',default=0.005,type=float,help="learning rate")
+        parser.add_argument('--update_fre',default=8,type=int)
+        parser.add_argument('--hidden_dim',default=36,type=int)
+        parser.add_argument('--device',default='cpu',type=str,help="cpu or cuda") 
+        parser.add_argument('--seed',default=1,type=int,help="seed") 
+        parser.add_argument('--save_fig',default=True,type=bool,help="if save figure or not")   
+        parser.add_argument('--show_fig',default=False,type=bool,help="if show 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(self,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'])
+        n_states = env.observation_space.shape[0]
+        n_actions = env.action_space.n  # action dimension
+        print(f"state dim: {n_states}, action dim: {n_actions}")
+        cfg.update({"n_states":n_states,"n_actions":n_actions}) # update to cfg paramters
+        model = PGNet(n_states,1,hidden_dim=cfg['hidden_dim'])
+        memory = PGReplay()
+        agent = PolicyGradient(model,memory,cfg)
+        return env,agent
+    def train(self,cfg,env,agent):
+        print("Start training!")
+        print(f"Env: {cfg['env_name']}, Algorithm: {cfg['algo_name']}, Device: {cfg['device']}")
+        rewards = []
+        for i_ep in range(cfg['train_eps']):
+            state = env.reset()
+            ep_reward = 0
+            for _ in count():
+                action = agent.sample_action(state) # sample action
+                next_state, reward, done, _ = env.step(action)
+                ep_reward += reward
+                if done:
+                    reward = 0
+                agent.memory.push((state,float(action),reward))
+                state = next_state
+                if done:
+                    print(f"Episode：{i_ep+1}/{cfg['train_eps']}, Reward:{ep_reward:.2f}")
+                    break
+            if (i_ep+1) % cfg['update_fre'] == 0:
+                agent.update()
+            rewards.append(ep_reward)
+        print('Finish training!')
+        env.close() # close environment
+        res_dic = {'episodes':range(len(rewards)),'rewards':rewards}
+        return res_dic
+                
+    def test(self,cfg,env,agent):
+        print("Start testing!")
+        print(f"Env: {cfg['env_name']}, Algorithm: {cfg['algo_name']}, Device: {cfg['device']}")
+        rewards = []
+        for i_ep in range(cfg['test_eps']):
+            state = env.reset()
+            ep_reward = 0
+            for _ in count():
+                action = agent.predict_action(state)
+                next_state, reward, done, _ = env.step(action)
+                ep_reward += reward
+                if done:
+                    reward = 0
+                state = next_state
+                if done:
+                    print(f"Episode: {i_ep+1}/{cfg['test_eps']}，Reward: {ep_reward:.2f}")
+                    break
+            rewards.append(ep_reward)
+        print("Finish testing!")
+        env.close()
+        return {'episodes':range(len(rewards)),'rewards':rewards}
+    
+if __name__ == "__main__":
+    main = Main()
+    main.run()
+
+

projects/codes/PolicyGradient/outputs/CartPole-v0/20220822-174059/results/params.json

@@ -1,16 +0,0 @@
-{
-    "algo_name": "PolicyGradient",
-    "env_name": "CartPole-v0",
-    "train_eps": 200,
-    "test_eps": 20,
-    "gamma": 0.99,
-    "lr": 0.005,
-    "update_fre": 8,
-    "hidden_dim": 36,
-    "device": "cpu",
-    "seed": 1,
-    "result_path": "/Users/jj/Desktop/rl-tutorials/codes/PolicyGradient/outputs/CartPole-v0/20220822-174059/results/",
-    "model_path": "/Users/jj/Desktop/rl-tutorials/codes/PolicyGradient/outputs/CartPole-v0/20220822-174059/models/",
-    "save_fig": true,
-    "show_fig": false
-}

projects/codes/PolicyGradient/outputs/CartPole-v0/20220825-205930/results/params.json

@@ -0,0 +1 @@
+{"algo_name": "PolicyGradient", "env_name": "CartPole-v0", "train_eps": 200, "test_eps": 20, "gamma": 0.99, "lr": 0.005, "update_fre": 8, "hidden_dim": 36, "device": "cpu", "seed": 1, "save_fig": true, "show_fig": false, "result_path": "/Users/jj/Desktop/rl-tutorials/codes/PolicyGradient/outputs/CartPole-v0/20220825-205930/results/", "model_path": "/Users/jj/Desktop/rl-tutorials/codes/PolicyGradient/outputs/CartPole-v0/20220825-205930/models/", "n_states": 4, "n_actions": 2}

projects/codes/PolicyGradient/pg.py

@@ -5,7 +5,7 @@ Author: John
 Email: johnjim0816@gmail.com
 Date: 2020-11-22 23:27:44
 LastEditor: John
-LastEditTime: 2022-08-22 17:35:34
+LastEditTime: 2022-08-25 20:58:59
 Discription: 
 Environment: 
 '''
@@ -19,12 +19,12 @@ import numpy as np
         
 class PolicyGradient:
     
-    def __init__(self, n_states,model,memory,cfg):
-        self.gamma = cfg.gamma
-        self.device = torch.device(cfg.device) 
+    def __init__(self, model,memory,cfg):
+        self.gamma = cfg['gamma']
+        self.device = torch.device(cfg['device']) 
         self.memory = memory
         self.policy_net = model.to(self.device)
-        self.optimizer = torch.optim.RMSprop(self.policy_net.parameters(), lr=cfg.lr)
+        self.optimizer = torch.optim.RMSprop(self.policy_net.parameters(), lr=cfg['lr'])
 
     def sample_action(self,state):
 

projects/codes/PolicyGradient/task0.py

@@ -1,139 +0,0 @@
-#!/usr/bin/env python
-# coding=utf-8
-'''
-Author: John
-Email: johnjim0816@gmail.com
-Date: 2020-11-22 23:21:53
-LastEditor: John
-LastEditTime: 2022-08-22 17:40:07
-Discription: 
-Environment: 
-'''
-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 gym
-import torch
-import datetime
-import argparse
-from itertools import count
-import torch.nn.functional as F
-from pg import PolicyGradient
-from common.utils import save_results, make_dir,all_seed,save_args,plot_rewards
-from common.models import MLP
-from common.memories import PGReplay
-
-
-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='PolicyGradient',type=str,help="name of algorithm")
-    parser.add_argument('--env_name',default='CartPole-v0',type=str,help="name of environment")
-    parser.add_argument('--train_eps',default=200,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('--lr',default=0.005,type=float,help="learning rate")
-    parser.add_argument('--update_fre',default=8,type=int)
-    parser.add_argument('--hidden_dim',default=36,type=int)
-    parser.add_argument('--device',default='cpu',type=str,help="cpu or cuda") 
-    parser.add_argument('--seed',default=1,type=int,help="seed") 
-    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")   
-    parser.add_argument('--show_fig',default=False,type=bool,help="if show figure or not")           
-    args = parser.parse_args([])                          
-    return args
-
-class PGNet(MLP):
-    ''' instead of outputing action, PG Net outputs propabilities of actions, we can use class inheritance from MLP here
-    '''
-    def forward(self, x):
-        x = F.relu(self.fc1(x))
-        x = F.relu(self.fc2(x))
-        x = F.sigmoid(self.fc3(x))
-        return x
-
-def env_agent_config(cfg):
-    env = gym.make(cfg.env_name)  
-    if cfg.seed !=0: # set random seed
-        all_seed(env,seed=cfg.seed)
-    n_states = env.observation_space.shape[0]
-    n_actions = env.action_space.n  # action dimension
-    print(f"state dim: {n_states}, action dim: {n_actions}")
-    model = PGNet(n_states,1,hidden_dim=cfg.hidden_dim)
-    memory = PGReplay()
-    agent = PolicyGradient(n_states,model,memory,cfg)
-    return env,agent
-
-def train(cfg,env,agent):
-    print('Start training!')
-    print(f'Env:{cfg.env_name}, Algo:{cfg.algo_name}, Device:{cfg.device}')
-    rewards = []
-    for i_ep in range(cfg.train_eps):
-        state = env.reset()
-        ep_reward = 0
-        for _ in count():
-            action = agent.sample_action(state) # sample action
-            next_state, reward, done, _ = env.step(action)
-            ep_reward += reward
-            if done:
-                reward = 0
-            agent.memory.push((state,float(action),reward))
-            state = next_state
-            if done:
-                print(f'Episode：{i_ep+1}/{cfg.train_eps}, Reward:{ep_reward:.2f}')
-                break
-        if (i_ep+1) % cfg.update_fre == 0:
-            agent.update()
-        rewards.append(ep_reward)
-    print('Finish training!')
-    env.close() # close environment
-    res_dic = {'episodes':range(len(rewards)),'rewards':rewards}
-    return res_dic
-            
-
-def test(cfg,env,agent):
-    print("start testing!")
-    print(f"Env: {cfg.env_name}, Algo: {cfg.algo_name}, Device: {cfg.device}")
-    rewards = []
-    for i_ep in range(cfg.test_eps):
-        state = env.reset()
-        ep_reward = 0
-        for _ in count():
-            action = agent.predict_action(state)
-            next_state, reward, done, _ = env.step(action)
-            ep_reward += reward
-            if done:
-                reward = 0
-            state = next_state
-            if done:
-                print(f'Episode: {i_ep+1}/{cfg.test_eps}，Reward: {ep_reward:.2f}')
-                break
-        rewards.append(ep_reward)
-    print("finish testing!")
-    env.close()
-    return {'episodes':range(len(rewards)),'rewards':rewards}
-    
-if __name__ == "__main__":
-    cfg = get_args()
-    env, agent = env_agent_config(cfg)
-    res_dic = train(cfg, env, agent)
-    save_args(cfg,path = cfg.result_path) # save parameters
-    agent.save_model(path = cfg.model_path)  # save models
-    save_results(res_dic, tag = 'train', path = cfg.result_path) # save results
-    plot_rewards(res_dic['rewards'], cfg, path = cfg.result_path,tag = "train")  # plot results
-    # testing
-    env, agent = env_agent_config(cfg) # create new env for testing, sometimes can ignore this step
-    agent.load_model(path = cfg.model_path)  # load model
-    res_dic = test(cfg, env, agent)
-    save_results(res_dic, tag='test',
-                 path = cfg.result_path)  
-    plot_rewards(res_dic['rewards'], cfg, path = cfg.result_path,tag = "test") 
-
-