84 lines
3.3 KiB
Python
84 lines
3.3 KiB
Python
import sys
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import os
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curr_path = os.path.dirname(os.path.abspath(__file__)) # 当前文件所在绝对路径
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parent_path = os.path.dirname(curr_path) # 父路径
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sys.path.append(parent_path) # 添加路径到系统路径
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import gym
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import torch
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import datetime
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from common.utils import save_results, make_dir
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from common.utils import plot_rewards, plot_rewards_cn
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from DQN.agent import DQN
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from DQN.train import train,test
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curr_time = datetime.datetime.now().strftime("%Y%m%d-%H%M%S") # 获取当前时间
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algo_name = "DQN" # 算法名称
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env_name = 'CartPole-v1' # 环境名称
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class DQNConfig:
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''' 算法相关参数设置
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'''
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def __init__(self):
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self.algo_name = algo_name # 算法名称
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self.env_name = env_name # 环境名称
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self.device = torch.device(
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"cuda" if torch.cuda.is_available() else "cpu") # 检测GPU
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self.train_eps = 200 # 训练的回合数
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self.test_eps = 30 # 测试的回合数
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# 超参数
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self.gamma = 0.95 # 强化学习中的折扣因子
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self.epsilon_start = 0.90 # e-greedy策略中初始epsilon
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self.epsilon_end = 0.01 # e-greedy策略中的终止epsilon
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self.epsilon_decay = 500 # e-greedy策略中epsilon的衰减率
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self.lr = 0.0001 # 学习率
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self.memory_capacity = 100000 # 经验回放的容量
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self.batch_size = 64 # mini-batch SGD中的批量大小
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self.target_update = 4 # 目标网络的更新频率
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self.hidden_dim = 256 # 网络隐藏层
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class PlotConfig:
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''' 绘图相关参数设置
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'''
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def __init__(self) -> None:
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self.algo_name = algo_name # 算法名称
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self.env_name = env_name # 环境名称
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self.device = torch.device(
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"cuda" if torch.cuda.is_available() else "cpu") # 检测GPU
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self.result_path = curr_path + "/outputs/" + self.env_name + \
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'/' + curr_time + '/results/' # 保存结果的路径
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self.model_path = curr_path + "/outputs/" + self.env_name + \
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'/' + curr_time + '/models/' # 保存模型的路径
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self.save = True # 是否保存图片
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def env_agent_config(cfg, seed=1):
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''' 创建环境和智能体
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'''
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env = gym.make(cfg.env_name) # 创建环境
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env.seed(seed) # 设置随机种子
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state_dim = env.observation_space.shape[0] # 状态数
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action_dim = env.action_space.n # 动作数
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agent = DQN(state_dim, action_dim, cfg) # 创建智能体
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return env, agent
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cfg = DQNConfig()
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plot_cfg = PlotConfig()
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# 训练
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env, agent = env_agent_config(cfg, seed=1)
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rewards, ma_rewards = train(cfg, env, agent)
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make_dir(plot_cfg.result_path, plot_cfg.model_path) # 创建保存结果和模型路径的文件夹
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agent.save(path=plot_cfg.model_path) # 保存模型
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save_results(rewards, ma_rewards, tag='train',
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path=plot_cfg.result_path) # 保存结果
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plot_rewards_cn(rewards, ma_rewards, plot_cfg, tag="train") # 画出结果
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# 测试
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env, agent = env_agent_config(cfg, seed=10)
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agent.load(path=plot_cfg.model_path) # 导入模型
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rewards, ma_rewards = test(cfg, env, agent)
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save_results(rewards, ma_rewards, tag='test',
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path=plot_cfg.result_path) # 保存结果
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plot_rewards_cn(rewards, ma_rewards, plot_cfg, tag="test") # 画出结果
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