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update codes

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This commit is contained in:
johnjim0816
2021-11-18 15:41:27 +08:00
parent 442e307b01
commit 129c0c65fa
103 changed files with 1025 additions and 558 deletions
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48
codes/A2C/task0_train.py
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@@ -1,8 +1,7 @@
import sys,os
curr_path = os.path.dirname(__file__)
parent_path = os.path.dirname(curr_path)
sys.path.append(parent_path) # add current terminal path to sys.path
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sys.path
import gym
import numpy as np
@@ -17,17 +16,28 @@ from common.plot import plot_rewards
curr_time = datetime.datetime.now().strftime("%Y%m%d-%H%M%S") # obtain current time
class A2CConfig:
def __init__(self) -> None:
self.algo='A2C'
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 models
self.n_envs = 8
self.gamma = 0.99
self.hidden_size = 256
self.algo='A2C' # 算法名称
self.env_name= 'CartPole-v0' # 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 = "DQN" # 算法名称
self.env_name = 'CartPole-v0' # 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 make_envs(env_name):
def _thunk():
env = gym.make(env_name)
@@ -57,11 +67,11 @@ def compute_returns(next_value, rewards, masks, gamma=0.99):
def train(cfg,envs):
env = gym.make(cfg.env) # a single env
env = gym.make(cfg.env_name) # a single env
env.seed(10)
state_dim = envs.observation_space.shape[0]
action_dim = envs.action_space.n
model = ActorCritic(state_dim, action_dim, cfg.hidden_size).to(cfg.device)
model = ActorCritic(state_dim, action_dim, cfg.hidden_dim).to(cfg.device)
optimizer = optim.Adam(model.parameters())
frame_idx = 0
test_rewards = []
@@ -112,9 +122,11 @@ def train(cfg,envs):
return test_rewards, test_ma_rewards
if __name__ == "__main__":
cfg = A2CConfig()
envs = [make_envs(cfg.env) for i in range(cfg.n_envs)]
envs = SubprocVecEnv(envs) # 8 env
plot_cfg = PlotConfig()
envs = [make_envs(cfg.env_name) for i in range(cfg.n_envs)]
envs = SubprocVecEnv(envs)
# 训练
rewards,ma_rewards = train(cfg,envs)
make_dir(cfg.result_path,cfg.model_path)
save_results(rewards,ma_rewards,tag='train',path=cfg.result_path)
plot_rewards(rewards,ma_rewards,tag="train",env=cfg.env,algo = cfg.algo,path=cfg.result_path)
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