121 lines
6.0 KiB
Python
121 lines
6.0 KiB
Python
import sys,os
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os.environ["KMP_DUPLICATE_LIB_OK"] = "TRUE" # avoid "OMP: Error #15: Initializing libiomp5md.dll, but found libiomp5md.dll already initialized."
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curr_path = os.path.dirname(os.path.abspath(__file__)) # current path
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parent_path = os.path.dirname(curr_path) # parent path
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sys.path.append(parent_path) # add path to system path
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import datetime
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import argparse
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import gym
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import torch
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import numpy as np
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from common.utils import all_seed
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from common.launcher import Launcher
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from common.memories import PGReplay
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from common.models import ActorCriticSoftmax
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from envs.register import register_env
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from a2c_2 import A2C_2
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class Main(Launcher):
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def get_args(self):
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curr_time = datetime.datetime.now().strftime("%Y%m%d-%H%M%S") # obtain current time
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parser = argparse.ArgumentParser(description="hyperparameters")
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parser.add_argument('--algo_name',default='A2C',type=str,help="name of algorithm")
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parser.add_argument('--env_name',default='CartPole-v0',type=str,help="name of environment")
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parser.add_argument('--train_eps',default=2000,type=int,help="episodes of training")
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parser.add_argument('--test_eps',default=20,type=int,help="episodes of testing")
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parser.add_argument('--ep_max_steps',default = 100000,type=int,help="steps per episode, much larger value can simulate infinite steps")
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parser.add_argument('--gamma',default=0.99,type=float,help="discounted factor")
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parser.add_argument('--lr',default=3e-4,type=float,help="learning rate")
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parser.add_argument('--actor_hidden_dim',default=256,type=int)
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parser.add_argument('--critic_hidden_dim',default=256,type=int)
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parser.add_argument('--device',default='cpu',type=str,help="cpu or cuda")
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parser.add_argument('--seed',default=10,type=int,help="seed")
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parser.add_argument('--show_fig',default=False,type=bool,help="if show figure or not")
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parser.add_argument('--save_fig',default=True,type=bool,help="if save figure or not")
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args = parser.parse_args()
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default_args = {'result_path':f"{curr_path}/outputs/{args.env_name}/{curr_time}/results/",
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'model_path':f"{curr_path}/outputs/{args.env_name}/{curr_time}/models/",
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}
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args = {**vars(args),**default_args} # type(dict)
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return args
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def env_agent_config(self,cfg):
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''' create env and agent
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'''
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register_env(cfg['env_name'])
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env = gym.make(cfg['env_name'])
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if cfg['seed'] !=0: # set random seed
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all_seed(env,seed=cfg["seed"])
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try: # state dimension
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n_states = env.observation_space.n # print(hasattr(env.observation_space, 'n'))
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except AttributeError:
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n_states = env.observation_space.shape[0] # print(hasattr(env.observation_space, 'shape'))
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n_actions = env.action_space.n # action dimension
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print(f"n_states: {n_states}, n_actions: {n_actions}")
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cfg.update({"n_states":n_states,"n_actions":n_actions}) # update to cfg paramters
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models = {'ActorCritic':ActorCriticSoftmax(cfg['n_states'],cfg['n_actions'], actor_hidden_dim = cfg['actor_hidden_dim'],critic_hidden_dim=cfg['critic_hidden_dim'])}
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memories = {'ACMemory':PGReplay()}
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agent = A2C_2(models,memories,cfg)
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return env,agent
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def train(self,cfg,env,agent):
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print("Start training!")
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print(f"Env: {cfg['env_name']}, Algorithm: {cfg['algo_name']}, Device: {cfg['device']}")
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rewards = [] # record rewards for all episodes
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steps = [] # record steps for all episodes
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for i_ep in range(cfg['train_eps']):
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ep_reward = 0 # reward per episode
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ep_step = 0 # step per episode
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ep_entropy = 0
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state = env.reset() # reset and obtain initial state
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for _ in range(cfg['ep_max_steps']):
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action, value, dist = agent.sample_action(state) # sample action
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next_state, reward, done, _ = env.step(action) # update env and return transitions
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log_prob = torch.log(dist.squeeze(0)[action])
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entropy = -np.sum(np.mean(dist.detach().numpy()) * np.log(dist.detach().numpy()))
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agent.memory.push((value,log_prob,reward)) # save transitions
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state = next_state # update state
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ep_reward += reward
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ep_entropy += entropy
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ep_step += 1
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if done:
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break
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agent.update(next_state,ep_entropy) # update agent
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rewards.append(ep_reward)
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steps.append(ep_step)
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if (i_ep+1)%10==0:
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print(f'Episode: {i_ep+1}/{cfg["train_eps"]}, Reward: {ep_reward:.2f}, Steps:{ep_step}')
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print("Finish training!")
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return {'episodes':range(len(rewards)),'rewards':rewards,'steps':steps}
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def test(self,cfg,env,agent):
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print("Start testing!")
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print(f"Env: {cfg['env_name']}, Algorithm: {cfg['algo_name']}, Device: {cfg['device']}")
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rewards = [] # record rewards for all episodes
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steps = [] # record steps for all episodes
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for i_ep in range(cfg['test_eps']):
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ep_reward = 0 # reward per episode
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ep_step = 0
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state = env.reset() # reset and obtain initial state
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for _ in range(cfg['ep_max_steps']):
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action,_,_ = agent.predict_action(state) # predict action
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next_state, reward, done, _ = env.step(action)
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state = next_state
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ep_reward += reward
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ep_step += 1
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if done:
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break
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rewards.append(ep_reward)
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steps.append(ep_step)
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print(f"Episode: {i_ep+1}/{cfg['test_eps']}, Steps:{ep_step}, Reward: {ep_reward:.2f}")
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print("Finish testing!")
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return {'episodes':range(len(rewards)),'rewards':rewards,'steps':steps}
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if __name__ == "__main__":
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main = Main()
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main.run()
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