hot update Double DQN
This commit is contained in:
johnjim0816
129
projects/codes/DoubleDQN/main.py
Normal file
129
projects/codes/DoubleDQN/main.py
Normal file
@@ -0,0 +1,129 @@
|
||||
#!/usr/bin/env python
|
||||
# coding=utf-8
|
||||
'''
|
||||
Author: JiangJi
|
||||
Email: johnjim0816@gmail.com
|
||||
Date: 2021-11-07 18:10:37
|
||||
LastEditor: JiangJi
|
||||
LastEditTime: 2022-08-29 23:33:31
|
||||
Discription:
|
||||
'''
|
||||
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 to system path
|
||||
|
||||
import gym
|
||||
import datetime
|
||||
import argparse
|
||||
|
||||
from common.utils import all_seed
|
||||
from common.models import MLP
|
||||
from common.memories import ReplayBufferQue
|
||||
from DoubleDQN.double_dqn import DoubleDQN
|
||||
from common.launcher import Launcher
|
||||
from envs.register import register_env
|
||||
class Main(Launcher):
|
||||
def get_args(self):
|
||||
''' 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='DoubleDQN',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")
|
||||
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=1,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")
|
||||
args = parser.parse_args()
|
||||
default_args = {'result_path':f"{curr_path}/outputs/{args.env_name}/{curr_time}/results/",
|
||||
'model_path':f"{curr_path}/outputs/{args.env_name}/{curr_time}/models/",
|
||||
}
|
||||
args = {**vars(args),**default_args} # type(dict)
|
||||
return args
|
||||
def env_agent_config(self,cfg):
|
||||
''' create env and agent
|
||||
'''
|
||||
register_env(cfg['env_name'])
|
||||
env = gym.make(cfg['env_name'])
|
||||
if cfg['seed'] !=0: # set random seed
|
||||
all_seed(env,seed=cfg["seed"])
|
||||
try: # state dimension
|
||||
n_states = env.observation_space.n # print(hasattr(env.observation_space, 'n'))
|
||||
except AttributeError:
|
||||
n_states = env.observation_space.shape[0] # print(hasattr(env.observation_space, 'shape'))
|
||||
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
|
||||
models = {'Qnet':MLP(n_states,n_actions,hidden_dim=cfg['hidden_dim'])}
|
||||
memories = {'Memory':ReplayBufferQue(cfg['memory_capacity'])}
|
||||
agent = DoubleDQN(models,memories,cfg)
|
||||
return env,agent
|
||||
|
||||
def train(self,cfg,env,agent):
|
||||
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']):
|
||||
action = agent.sample_action(state)
|
||||
next_state, reward, done, _ = env.step(action)
|
||||
ep_reward += reward
|
||||
agent.memory.push((state, action, reward, next_state, done))
|
||||
state = next_state
|
||||
agent.update()
|
||||
if done:
|
||||
break
|
||||
if i_ep % cfg['target_update'] == 0:
|
||||
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(self,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']):
|
||||
action = agent.predict_action(state)
|
||||
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__":
|
||||
main = Main()
|
||||
main.run()
|
||||
Reference in New Issue
Block a user