134 lines
6.0 KiB
Python
134 lines
6.0 KiB
Python
import sys,os
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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 gym
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import torch
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import datetime
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import numpy as np
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import argparse
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from common.utils import save_results,all_seed
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from common.utils import plot_rewards,save_args
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from common.models import MLP
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from common.memories import ReplayBuffer
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from dqn import DQN
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def get_args():
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""" hyperparameters
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"""
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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='DQN',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=200,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('--gamma',default=0.95,type=float,help="discounted factor")
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parser.add_argument('--epsilon_start',default=0.95,type=float,help="initial value of epsilon")
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parser.add_argument('--epsilon_end',default=0.01,type=float,help="final value of epsilon")
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parser.add_argument('--epsilon_decay',default=500,type=int,help="decay rate of epsilon")
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parser.add_argument('--lr',default=0.0001,type=float,help="learning rate")
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parser.add_argument('--memory_capacity',default=100000,type=int,help="memory capacity")
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parser.add_argument('--batch_size',default=64,type=int)
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parser.add_argument('--target_update',default=4,type=int)
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parser.add_argument('--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('--result_path',default=curr_path + "/outputs/" + parser.parse_args().env_name + \
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'/' + curr_time + '/results' )
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parser.add_argument('--model_path',default=curr_path + "/outputs/" + parser.parse_args().env_name + \
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'/' + curr_time + '/models' )
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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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return args
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def env_agent_config(cfg):
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''' create env and agent
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'''
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env = gym.make(cfg.env_name) # create env
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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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n_states = env.observation_space.shape[0] # state dimension
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n_actions = env.action_space.n # action dimension
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print(f"state dim: {n_states}, action dim: {n_actions}")
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model = MLP(n_states,n_actions,hidden_dim=cfg.hidden_dim)
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memory = ReplayBuffer(cfg.memory_capacity) # replay buffer
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agent = DQN(n_actions,model,memory,cfg) # create agent
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return env, agent
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def train(cfg, env, agent):
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''' 训练
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'''
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print("start training!")
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print(f"Env: {cfg.env_name}, Algo: {cfg.algo_name}, Device: {cfg.device}")
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rewards = [] # record rewards for all episodes
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steps = []
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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
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state = env.reset() # reset and obtain initial state
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while True:
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ep_step += 1
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action = 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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agent.memory.push(state, action, reward,
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next_state, done) # save transitions
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state = next_state # update next state for env
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agent.update() # update agent
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ep_reward += reward #
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if done:
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break
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if (i_ep + 1) % cfg.target_update == 0: # target net update, target_update means "C" in pseucodes
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agent.target_net.load_state_dict(agent.policy_net.state_dict())
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steps.append(ep_step)
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rewards.append(ep_reward)
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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}: Epislon: {agent.epsilon:.3f}')
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print("finish training!")
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env.close()
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res_dic = {'episodes':range(len(rewards)),'rewards':rewards}
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return res_dic
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def test(cfg, env, agent):
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print("start testing!")
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print(f"Env: {cfg.env_name}, Algo: {cfg.algo_name}, Device: {cfg.device}")
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rewards = [] # record rewards for all episodes
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steps = []
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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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while True:
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ep_step+=1
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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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if done:
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break
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steps.append(ep_step)
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rewards.append(ep_reward)
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print(f'Episode: {i_ep+1}/{cfg.test_eps}，Reward: {ep_reward:.2f}')
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print("finish testing!")
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env.close()
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return {'episodes':range(len(rewards)),'rewards':rewards}
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if __name__ == "__main__":
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cfg = get_args()
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# training
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env, agent = env_agent_config(cfg)
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res_dic = train(cfg, env, agent)
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save_args(cfg,path = cfg.result_path) # save parameters
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agent.save_model(path = cfg.model_path) # save models
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save_results(res_dic, tag = 'train', path = cfg.result_path) # save results
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plot_rewards(res_dic['rewards'], cfg, path = cfg.result_path,tag = "train") # plot results
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# testing
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env, agent = env_agent_config(cfg) # create new env for testing, sometimes can ignore this step
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agent.load_model(path = cfg.model_path) # load model
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res_dic = test(cfg, env, agent)
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save_results(res_dic, tag='test',
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path = cfg.result_path)
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plot_rewards(res_dic['rewards'], cfg, path = cfg.result_path,tag = "test")
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