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hot update DQN

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johnjim0816
2022-08-24 12:49:16 +08:00
parent 07fb1d233e
commit 4f4658503e
24 changed files with 148 additions and 512 deletions
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projects/codes/DQN/task0.py
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import sys,os
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('--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' )
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()
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"state dim: {n_states}, action dim: {n_actions}")
model = MLP(n_states,n_actions,hidden_dim=cfg.hidden_dim)
memory = ReplayBuffer(cfg.memory_capacity) # replay buffer
agent = DQN(n_actions,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")