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johnjim0816
2022-07-13 23:52:05 +08:00
parent 45cc4aff58
commit bab7f6fe8c
66 changed files with 247 additions and 841 deletions
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91
codes/A2C/task0.py
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@@ -1,45 +1,43 @@
import sys
import os
curr_path = os.path.dirname(os.path.abspath(__file__)) # 当前文件所在绝对路径
parent_path = os.path.dirname(curr_path) # 父路径
sys.path.append(parent_path) # 添加路径到系统路径
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 numpy as np
import torch
import torch.optim as optim
import datetime
import argparse
from common.multiprocessing_env import SubprocVecEnv
from a2c import ActorCritic
from common.utils import save_results, make_dir
from common.utils import plot_rewards
from common.utils import plot_rewards, save_args
curr_time = datetime.datetime.now().strftime("%Y%m%d-%H%M%S") # 获取当前时间
algo_name = 'A2C' # 算法名称
env_name = 'CartPole-v0' # 环境名称
class A2CConfig:
def __init__(self) -> None:
self.algo_name = algo_name# 算法名称
self.env_name = env_name # 环境名称
self.n_envs = 8 # 异步的环境数目
self.gamma = 0.99 # 强化学习中的折扣因子
self.hidden_dim = 256
self.lr = 1e-3 # learning rate
self.max_frames = 30000
self.n_steps = 5
self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
class PlotConfig:
def __init__(self) -> None:
self.algo_name = algo_name # 算法名称
self.env_name = env_name # 环境名称
self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu") # 检测GPU
self.result_path = curr_path+"/outputs/" + self.env_name + \
'/'+curr_time+'/results/' # 保存结果的路径
self.model_path = curr_path+"/outputs/" + self.env_name + \
'/'+curr_time+'/models/' # 保存模型的路径
self.save = True # 是否保存图片
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='A2C',type=str,help="name of algorithm")
parser.add_argument('--env_name',default='CartPole-v0',type=str,help="name of environment")
parser.add_argument('--n_envs',default=8,type=int,help="numbers of environments")
parser.add_argument('--max_steps',default=20000,type=int,help="episodes of training")
parser.add_argument('--n_steps',default=5,type=int,help="episodes of testing")
parser.add_argument('--gamma',default=0.99,type=float,help="discounted factor")
parser.add_argument('--lr',default=1e-3,type=float,help="learning rate")
parser.add_argument('--hidden_dim',default=256,type=int)
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/' ) # path to save models
parser.add_argument('--save_fig',default=True,type=bool,help="if save figure or not")
args = parser.parse_args()
args.device = torch.device(
"cuda" if torch.cuda.is_available() else "cpu") # check GPU
return args
def make_envs(env_name):
def _thunk():
@@ -60,6 +58,7 @@ def test_env(env,model,vis=False):
if vis: env.render()
total_reward += reward
return total_reward
def compute_returns(next_value, rewards, masks, gamma=0.99):
R = next_value
returns = []
@@ -70,19 +69,19 @@ def compute_returns(next_value, rewards, masks, gamma=0.99):
def train(cfg,envs):
print('开始训练!')
print(f'环境：{cfg.env_name}, 算法：{cfg.algo}, 设备：{cfg.device}')
print('Start training!')
print(f'Env:{cfg.env_name}, Algorithm:{cfg.algo_name}, Device:{cfg.device}')
env = gym.make(cfg.env_name) # a single env
env.seed(10)
n_states = envs.observation_space.shape[0]
n_actions = envs.action_space.n
model = ActorCritic(n_states, n_actions, cfg.hidden_dim).to(cfg.device)
optimizer = optim.Adam(model.parameters())
frame_idx = 0
step_idx = 0
test_rewards = []
test_ma_rewards = []
state = envs.reset()
while frame_idx < cfg.max_frames:
while step_idx < cfg.max_steps:
log_probs = []
values = []
rewards = []
@@ -101,16 +100,16 @@ def train(cfg,envs):
rewards.append(torch.FloatTensor(reward).unsqueeze(1).to(cfg.device))
masks.append(torch.FloatTensor(1 - done).unsqueeze(1).to(cfg.device))
state = next_state
frame_idx += 1
if frame_idx % 100 == 0:
step_idx += 1
if step_idx % 100 == 0:
test_reward = np.mean([test_env(env,model) for _ in range(10)])
print(f"frame_idx:{frame_idx}, test_reward:{test_reward}")
print(f"step_idx:{step_idx}, test_reward:{test_reward}")
test_rewards.append(test_reward)
if test_ma_rewards:
test_ma_rewards.append(0.9*test_ma_rewards[-1]+0.1*test_reward)
else:
test_ma_rewards.append(test_reward)
# plot(frame_idx, test_rewards)
# plot(step_idx, test_rewards)
next_state = torch.FloatTensor(next_state).to(cfg.device)
_, next_value = model(next_state)
returns = compute_returns(next_value, rewards, masks)
@@ -124,15 +123,15 @@ def train(cfg,envs):
optimizer.zero_grad()
loss.backward()
optimizer.step()
print('完成训练！')
print('Finish training！')
return test_rewards, test_ma_rewards
if __name__ == "__main__":
cfg = A2CConfig()
plot_cfg = PlotConfig()
cfg = get_args()
envs = [make_envs(cfg.env_name) for i in range(cfg.n_envs)]
envs = SubprocVecEnv(envs)
# 训练
# training
rewards,ma_rewards = train(cfg,envs)
make_dir(plot_cfg.result_path,plot_cfg.model_path)
save_results(rewards, ma_rewards, tag='train', path=plot_cfg.result_path) # 保存结果
plot_rewards(rewards, ma_rewards, plot_cfg, tag="train") # 画出结果
make_dir(cfg.result_path,cfg.model_path)
save_args(cfg)
save_results(rewards, ma_rewards, tag='train', path=cfg.result_path) # 保存结果
plot_rewards(rewards, ma_rewards, cfg, tag="train") # 画出结果