easy-rl/codes/DQN/task0_train.ipynb

In [1]:

import sys
from pathlib import Path
curr_path = str(Path().absolute())
parent_path = str(Path().absolute().parent)
sys.path.append(parent_path) # add current terminal path to sys.path

In [2]:

import gym
import torch
import datetime

from common.utils import save_results, make_dir
from common.plot import plot_rewards
from DQN.agent import DQN

curr_time = datetime.datetime.now().strftime(
    "%Y%m%d-%H%M%S")  # obtain current time

In [3]:

class DQNConfig:
    def __init__(self):
        self.algo = "DQN"  # name of algo
        self.env = 'CartPole-v0'
        self.result_path = curr_path+"/outputs/" + self.env + \
            '/'+curr_time+'/results/'  # path to save results
        self.model_path = curr_path+"/outputs/" + self.env + \
            '/'+curr_time+'/models/'  # path to save results
        self.train_eps = 300  # max trainng episodes
        self.eval_eps = 50 # number of episodes for evaluating
        self.gamma = 0.95
        self.epsilon_start = 0.90  # start epsilon of e-greedy policy
        self.epsilon_end = 0.01
        self.epsilon_decay = 500
        self.lr = 0.0001  # learning rate
        self.memory_capacity = 100000  # capacity of Replay Memory
        self.batch_size = 64
        self.target_update = 2 # update frequency of target net
        self.device = torch.device(
            "cuda" if torch.cuda.is_available() else "cpu")  # check gpu
        self.hidden_dim = 256  # hidden size of net

In [4]:

def env_agent_config(cfg,seed=1):
    env = gym.make(cfg.env)  
    env.seed(seed)
    state_dim = env.observation_space.shape[0]
    action_dim = env.action_space.n
    agent = DQN(state_dim,action_dim,cfg)
    return env,agent

In [5]:

def train(cfg, env, agent):
    print('Start to train !')
    print(f'Env:{cfg.env}, Algorithm:{cfg.algo}, Device:{cfg.device}')
    rewards = []
    ma_rewards = []  # moveing average reward
    for i_ep in range(cfg.train_eps):
        state = env.reset()
        done = False
        ep_reward = 0
        while True:
            action = agent.choose_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())
        if (i_ep+1)%10 == 0:
            print('Episode:{}/{}, Reward:{}'.format(i_ep+1, cfg.train_eps, ep_reward))
        rewards.append(ep_reward)
        # save ma rewards
        if ma_rewards:
            ma_rewards.append(0.9*ma_rewards[-1]+0.1*ep_reward)
        else:
            ma_rewards.append(ep_reward)
    print('Complete training！')
    return rewards, ma_rewards

In [6]:

def eval(cfg,env,agent):
    rewards = []  
    ma_rewards = [] # moving average rewards
    for i_ep in range(cfg.eval_eps):
        ep_reward = 0  # reward per episode
        state = env.reset()  
        while True:
            action = agent.predict(state) 
            next_state, reward, done, _ = env.step(action)  
            state = next_state  
            ep_reward += reward
            if done:
                break
        rewards.append(ep_reward)
        if ma_rewards:
            ma_rewards.append(ma_rewards[-1]*0.9+ep_reward*0.1)
        else:
            ma_rewards.append(ep_reward)
        if (i_ep+1)%10==0:
            print(f"Episode:{i_ep+1}/{cfg.eval_eps}, reward:{ep_reward:.1f}")
    return rewards,ma_rewards

In [7]:

if __name__ == "__main__":
    cfg = DQNConfig()

    # train
    env,agent = env_agent_config(cfg,seed=1)
    rewards, ma_rewards = train(cfg, env, agent)
    make_dir(cfg.result_path, cfg.model_path)
    agent.save(path=cfg.model_path)
    save_results(rewards, ma_rewards, tag='train', path=cfg.result_path)
    plot_rewards(rewards, ma_rewards, tag="train",
                 algo=cfg.algo, path=cfg.result_path)
    # eval
    env,agent = env_agent_config(cfg,seed=10)
    agent.load(path=cfg.model_path)
    rewards,ma_rewards = eval(cfg,env,agent)
    save_results(rewards,ma_rewards,tag='eval',path=cfg.result_path)
    plot_rewards(rewards,ma_rewards,tag="eval",env=cfg.env,algo = cfg.algo,path=cfg.result_path)

Start to train !
Env:CartPole-v0, Algorithm:DQN, Device:cuda
Episode:10/300, Reward:13.0
Episode:20/300, Reward:14.0
Episode:30/300, Reward:14.0
Episode:40/300, Reward:12.0
Episode:50/300, Reward:125.0
Episode:60/300, Reward:98.0
Episode:70/300, Reward:200.0
Episode:80/300, Reward:160.0
Episode:90/300, Reward:200.0
Episode:100/300, Reward:200.0
Episode:110/300, Reward:200.0
Episode:120/300, Reward:198.0
Episode:130/300, Reward:200.0
Episode:140/300, Reward:200.0
Episode:150/300, Reward:200.0
Episode:160/300, Reward:200.0
Episode:170/300, Reward:200.0
Episode:180/300, Reward:200.0
Episode:190/300, Reward:200.0
Episode:200/300, Reward:200.0
Episode:210/300, Reward:200.0
Episode:220/300, Reward:200.0
Episode:230/300, Reward:188.0
Episode:240/300, Reward:200.0
Episode:250/300, Reward:200.0
Episode:260/300, Reward:193.0
Episode:270/300, Reward:200.0
Episode:280/300, Reward:200.0
Episode:290/300, Reward:200.0
Episode:300/300, Reward:200.0
Complete training！
results saved!

Episode:10/50, reward:188.0
Episode:20/50, reward:200.0
Episode:30/50, reward:200.0
Episode:40/50, reward:200.0
Episode:50/50, reward:171.0
results saved!