OsuStdEnv.py

import gym
from gym import spaces
import numpy as np
from gym.envs.registration import register

class ScreenEnv(gym.Env):
    metadata = {'render.modes': ['human']}
    

    def __init__(self):
        super(ScreenEnv, self).__init__()
        screen_len = 640
        screen_height = 480
        
        # 假设屏幕大小为640x480，图像为灰度图
        self.observation_space = spaces.Box(low=0, high=255, shape=(screen_height, screen_len), dtype=np.uint8)
        
        # 动作空间定义：x, y坐标和z, x键的按下状态
        self.action_space = spaces.Dict({
            "x": spaces.Box(low=0, high=screen_len-1, shape=(), dtype=np.int32),
            "y": spaces.Box(low=0, high=screen_height-1, shape=(), dtype=np.int32),
            "z_key": spaces.Discrete(2),
            "x_key": spaces.Discrete(2),
        })

    def step(self, action):
        # 实现环境的一步动作逻辑
        # 这里需要根据action来更新环境状态，并返回新的观察、奖励、完成状态和额外信息
        # 示例代码省略了实际逻辑
        observation = np.random.randint(0, 256, self.observation_space.shape).astype(np.uint8)
        reward = 0
        done = False
        info = {}
        return observation, reward, done, info

    def reset(self):
        # 重置环境状态到初始状态并返回初始观察
        # 示例代码省略了实际逻辑
        return np.random.randint(0, 256, self.observation_space.shape).astype(np.uint8)

    def render(self, mode='human', close=False):
        # 可视化环境状态
        pass  # 可视化代码省略


register(
    id='OsuStdEnv',
    entry_point='your_module.ScreenEnv',
)