139 lines
6.6 KiB
Python
139 lines
6.6 KiB
Python
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('--ep_max_steps',default = 100000,type=int,help="steps per episode, much larger value can simulate infinite steps")
|
||
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, the higher value, the slower decay")
|
||
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']}, Algorithm: {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
|
||
for _ in range(cfg['ep_max_steps']):
|
||
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,'steps':steps}
|
||
return res_dic
|
||
|
||
def test(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 = []
|
||
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']):
|
||
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,'steps':steps}
|
||
|
||
|
||
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")
|