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 gym import torch import datetime import numpy as np import argparse from common.utils import save_results,all_seed from common.utils import plot_rewards,save_args from common.models import MLP from common.memories import ReplayBuffer from dqn import DQN 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='DQN',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=0.95,type=float,help="discounted factor") parser.add_argument('--epsilon_start',default=0.95,type=float,help="initial value of epsilon") parser.add_argument('--epsilon_end',default=0.01,type=float,help="final value of epsilon") parser.add_argument('--epsilon_decay',default=500,type=int,help="decay rate of epsilon") parser.add_argument('--lr',default=0.0001,type=float,help="learning rate") parser.add_argument('--memory_capacity',default=100000,type=int,help="memory capacity") parser.add_argument('--batch_size',default=64,type=int) parser.add_argument('--target_update',default=4,type=int) parser.add_argument('--hidden_dim',default=256,type=int) 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") # please manually change the following args in this script if you want 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' ) args = parser.parse_args() args = {**vars(args)} # type(dict) return args def env_agent_config(cfg): ''' create env and agent ''' env = gym.make(cfg['env_name']) # create env if cfg['seed'] !=0: # set random seed all_seed(env,seed=cfg["seed"]) n_states = env.observation_space.shape[0] # state dimension 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 model = MLP(n_states,n_actions,hidden_dim=cfg["hidden_dim"]) memory = ReplayBuffer(cfg["memory_capacity"]) # replay buffer agent = DQN(model,memory,cfg) # create agent return env, agent def train(cfg, env, agent): ''' 训练 ''' print("start training!") print(f"Env: {cfg['env_name']}, Algo: {cfg['algo_name']}, Device: {cfg['device']}") rewards = [] # record rewards for all episodes steps = [] for i_ep in range(cfg["train_eps"]): ep_reward = 0 # reward per episode ep_step = 0 state = env.reset() # reset and obtain initial state while True: ep_step += 1 action = agent.sample_action(state) # sample action next_state, reward, done, _ = env.step(action) # update env and return transitions agent.memory.push(state, action, reward, next_state, done) # save transitions state = next_state # update next state for env agent.update() # update agent ep_reward += reward # if done: break if (i_ep + 1) % cfg["target_update"] == 0: # target net update, target_update means "C" in pseucodes agent.target_net.load_state_dict(agent.policy_net.state_dict()) steps.append(ep_step) rewards.append(ep_reward) if (i_ep + 1) % 10 == 0: print(f'Episode: {i_ep+1}/{cfg["train_eps"]}, Reward: {ep_reward:.2f}: Epislon: {agent.epsilon:.3f}') print("finish training!") env.close() 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 = [] # record rewards for all episodes steps = [] 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 while True: ep_step+=1 action = agent.predict_action(state) # predict action next_state, reward, done, _ = env.step(action) state = next_state ep_reward += reward if done: break steps.append(ep_step) rewards.append(ep_reward) print(f'Episode: {i_ep+1}/{cfg.test_eps}，Reward: {ep_reward:.2f}') print("finish testing!") env.close() return {'episodes':range(len(rewards)),'rewards':rewards} if __name__ == "__main__": cfg = get_args() # training 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")