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()
+
+