OpenAI Gym中FrozenLake环境（场景）源码分析（7）

**OpenAI Gym 中 FrozenLake 环境源码分析**

在本文中，我们将深入分析 OpenAI Gym 中的 FrozenLake 环境源码。FrozenLake 是一个经典的控制问题，涉及一个小球在一个4x4 的冰湖上移动。环境的目标是让小球从起始位置移动到终止位置。

**环境描述**

FrozenLake 环境由以下组成：

* **状态空间**: 小球可以位于16 个不同的格子中，每个格子代表一个状态。
* **动作空间**: 小球可以向上、下、左、右或不动移动。
* **奖励函数**: 每次小球移动到终止位置时，环境会给出 +1 的奖励。否则，奖励为0。
* **终止条件**: 当小球移动到终止位置时，环境会结束。

**FrozenLake 环境源码**

下面是 FrozenLake 环境的源码：

import numpy as npclass FrozenLakeEnv(gym.Env):
 """
 A simple gridworld environment where the agent must navigate from a start state to a goal state.
 The environment is a4x4 grid, with some cells being slippery (i.e., they will cause the agent to slip and move in a different direction).
 The agent can move up, down, left, right or stay still. If it moves into an obstacle cell, it will be pushed back.
 The reward is +1 if the agent reaches the goal state,0 otherwise.
 The episode ends when the agent reaches the goal state or slips out of bounds.
 """
 metadata = {'render.modes': ['human']}
 def __init__(self):
 self.action_space = gym.spaces.Discrete(4) # up, down, left, right self.observation_space = gym.spaces.Box(low=0, high=15, shape=(1,), dtype=np.int32)
 self.grid_size =4 self.slippery_cells = [(3,0), (2,1), (0,2)]
 self.agent_position = np.array([0,0])
 self.goal_position = np.array([3,3])
 def step(self, action):
 """
 Take an action in the environment.
 Args:
 action: The action to take. Can be one of up, down, left, right or stay still.
 Returns:
 observation: The new state of the agent.
 reward: The reward for taking this action.
 done: Whether the episode has ended.
 info: Any additional information about the step.
 """
 # Get the current position of the agent x, y = self.agent_position # Move the agent based on the action taken if action ==0 and (x >0 or (x, y) in self.slippery_cells):
 x -=1 elif action ==1 and (y < self.grid_size -1 or (x, y) in self.slippery_cells):
 y +=1 elif action ==2 and (x < self.grid_size -1 or (x, y) in self.slippery_cells):
 x +=1 elif action ==3 and (y >0 or (x, y) in self.slippery_cells):
 y -=1 # Check if the agent has reached the goal state if np.array_equal(self.agent_position, self.goal_position):
 reward =1.0 done = True else:
 reward =0.0 done = False # Update the observation space self.observation_space = gym.spaces.Box(low=0, high=self.grid_size -1, shape=(1,), dtype=np.int32)
 return np.array([x]), reward, done, {}
 def reset(self):
 """
 Reset the environment to its initial state.
 Returns:
 observation: The new state of the agent.
 """
 self.agent_position = np.array([0,0])
 self.observation_space = gym.spaces.Box(low=0, high=self.grid_size -1, shape=(1,), dtype=np.int32)
 return np.array([0])
 def render(self, mode='human'):
 """
 Render the environment.
 Args:
 mode: The rendering mode. Can be one of human or rgb_array.
 """
 # Create a4x4 grid grid = [['.' for _ in range(4)] for _ in range(4)]
 # Mark the agent position on the grid x, y = self.agent_position grid[x][y] = 'A'
 # Mark the goal position on the grid x, y = self.goal_position grid[x][y] = 'G'
 # Print the grid for row in grid:
 print(' '.join(row))

**环境分析**

FrozenLake 环境是一个经典的控制问题，涉及一个小球在一个4x4 的冰湖上移动。环境的目标是让小球从起始位置移动到终止位置。

环境由以下组成：

* **状态空间**: 小球可以位于16 个不同的格子中，每个格子代表一个状态。
* **动作空间**: 小球可以向上、下、左、右或不动移动。
* **奖励函数**: 每次小球移动到终止位置时，环境会给出 +1 的奖励。否则，奖励为0。
* **终止条件**: 当小球移动到终止位置时，环境会结束。

**环境源码分析**

FrozenLake 环境的源码由以下组成：

* **类定义**: `FrozenLakeEnv` 类定义了 FrozenLake 环境的属性和方法。
* **属性定义**: `action_space` 和 `observation_space` 属性定义了环境的动作空间和状态空间。
* **方法定义**: `step` 方法定义了环境的步骤逻辑，包括小球移动、奖励计算和终止条件检查。`reset` 方法定义了环境的重置逻辑，包括小球位置的重置。`render` 方法定义了环境的渲染逻辑，包括小球位置和目标位置的标记。

**总结**

FrozenLake 环境是一个经典的控制问题，涉及一个小球在一个4x4 的冰湖上移动。环境的目标是让小球从起始位置移动到终止位置。环境由状态空间、动作空间、奖励函数和终止条件组成。FrozenLake 环境的源码定义了环境的属性和方法，包括步骤逻辑、重置逻辑和渲染逻辑。