hot update A2C

2022-08-29 15:12:33 +08:00
parent 99a3c1afec
commit 0b0f7e857d
109 changed files with 8213 additions and 1658 deletions
--- a/projects/codes/A2C/main.py
+++ b/projects/codes/A2C/main.py
@@ -0,0 +1,121 @@
+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 datetime
+import argparse
+import gym
+import torch
+import numpy as np
+from common.utils import all_seed
+from common.launcher import Launcher
+from common.memories import PGReplay
+from common.models import ActorSoftmax,Critic
+from envs.register import register_env
+from a2c import A2C
+
+class Main(Launcher):
+    def get_args(self):
+        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='A2C',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=1600,type=int,help="episodes of training") 
+        parser.add_argument('--test_eps',default=20,type=int,help="episodes of testing") 
+        parser.add_argument('--ep_max_steps',default = 100000,type=int,help="steps per episode, much larger value can simulate infinite steps")
+        parser.add_argument('--gamma',default=0.99,type=float,help="discounted factor") 
+        parser.add_argument('--actor_lr',default=3e-4,type=float,help="learning rate of actor")
+        parser.add_argument('--critic_lr',default=1e-3,type=float,help="learning rate of critic")
+        parser.add_argument('--actor_hidden_dim',default=256,type=int,help="hidden of actor net")
+        parser.add_argument('--critic_hidden_dim',default=256,type=int,help="hidden of critic net")
+        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(self,cfg):
+        ''' create env and agent
+        '''  
+        register_env(cfg['env_name'])
+        env = gym.make(cfg['env_name']) 
+        if cfg['seed'] !=0: # set random seed
+            all_seed(env,seed=cfg["seed"]) 
+        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
+        models = {'Actor':ActorSoftmax(cfg['n_states'],cfg['n_actions'], hidden_dim = cfg['actor_hidden_dim']),'Critic':Critic(cfg['n_states'],1,hidden_dim=cfg['critic_hidden_dim'])}
+        memories = {'ACMemory':PGReplay()}
+        agent = A2C(models,memories,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 = []  # 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
+            ep_entropy = 0
+            state = env.reset()  # reset and obtain initial state
+            
+            for _ in range(cfg['ep_max_steps']):
+                action, value, dist = agent.sample_action(state)  # sample action
+                next_state, reward, done, _ = env.step(action)  # update env and return transitions
+                log_prob = torch.log(dist.squeeze(0)[action])
+                entropy = -np.sum(np.mean(dist.detach().numpy()) * np.log(dist.detach().numpy()))
+                agent.memory.push((value,log_prob,reward))  # save transitions
+                state = next_state  # update state
+                ep_reward += reward
+                ep_entropy += entropy
+                ep_step += 1
+                if done:
+                    break
+            agent.update(next_state,ep_entropy)  # update agent
+            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}')
+        print("Finish training!")
+        return {'episodes':range(len(rewards)),'rewards':rewards,'steps':steps}
+    def test(self,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
+            state = env.reset()  # reset and obtain initial state
+            for _ in range(cfg['ep_max_steps']):
+                action,_,_ = agent.predict_action(state)  # predict action
+                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__":
+    main = Main()
+    main.run()
+   
+
+        
+