Gym-MiniGrid/gym_minigrid/envs/memory.py

import numpy as np

from gym_minigrid.minigrid import Ball, Grid, Key, MiniGridEnv, Wall


class MemoryEnv(MiniGridEnv):
    """
    This environment is a memory test. The agent starts in a small room
    where it sees an object. It then has to go through a narrow hallway
    which ends in a split. At each end of the split there is an object,
    one of which is the same as the object in the starting room. The
    agent has to remember the initial object, and go to the matching
    object at split.
    """

    def __init__(self, size=8, random_length=False, **kwargs):
        self.random_length = random_length
        super().__init__(
            grid_size=size,
            max_steps=5 * size**2,
            # Set this to True for maximum speed
            see_through_walls=False,
            **kwargs
        )

    def _gen_grid(self, width, height):
        self.grid = Grid(width, height)

        # Generate the surrounding walls
        self.grid.horz_wall(0, 0)
        self.grid.horz_wall(0, height - 1)
        self.grid.vert_wall(0, 0)
        self.grid.vert_wall(width - 1, 0)

        assert height % 2 == 1
        upper_room_wall = height // 2 - 2
        lower_room_wall = height // 2 + 2
        if self.random_length:
            hallway_end = self._rand_int(4, width - 2)
        else:
            hallway_end = width - 3

        # Start room
        for i in range(1, 5):
            self.grid.set(i, upper_room_wall, Wall())
            self.grid.set(i, lower_room_wall, Wall())
        self.grid.set(4, upper_room_wall + 1, Wall())
        self.grid.set(4, lower_room_wall - 1, Wall())

        # Horizontal hallway
        for i in range(5, hallway_end):
            self.grid.set(i, upper_room_wall + 1, Wall())
            self.grid.set(i, lower_room_wall - 1, Wall())

        # Vertical hallway
        for j in range(0, height):
            if j != height // 2:
                self.grid.set(hallway_end, j, Wall())
            self.grid.set(hallway_end + 2, j, Wall())

        # Fix the player's start position and orientation
        self.agent_pos = np.array((self._rand_int(1, hallway_end + 1), height // 2))
        self.agent_dir = 0

        # Place objects
        start_room_obj = self._rand_elem([Key, Ball])
        self.grid.set(1, height // 2 - 1, start_room_obj("green"))

        other_objs = self._rand_elem([[Ball, Key], [Key, Ball]])
        pos0 = (hallway_end + 1, height // 2 - 2)
        pos1 = (hallway_end + 1, height // 2 + 2)
        self.grid.set(*pos0, other_objs[0]("green"))
        self.grid.set(*pos1, other_objs[1]("green"))

        # Choose the target objects
        if start_room_obj == other_objs[0]:
            self.success_pos = (pos0[0], pos0[1] + 1)
            self.failure_pos = (pos1[0], pos1[1] - 1)
        else:
            self.success_pos = (pos1[0], pos1[1] - 1)
            self.failure_pos = (pos0[0], pos0[1] + 1)

        self.mission = "go to the matching object at the end of the hallway"

    def step(self, action):
        if action == MiniGridEnv.Actions.pickup:
            action = MiniGridEnv.Actions.toggle
        obs, reward, done, info = MiniGridEnv.step(self, action)

        if tuple(self.agent_pos) == self.success_pos:
            reward = self._reward()
            done = True
        if tuple(self.agent_pos) == self.failure_pos:
            reward = 0
            done = True

        return obs, reward, done, info