import itertools as itt import numpy as np from gym_minigrid.minigrid import Goal, Grid, Lava, MiniGridEnv, Wall from gym_minigrid.register import register class CrossingEnv(MiniGridEnv): """ Environment with wall or lava obstacles, sparse reward. """ def __init__(self, size=9, num_crossings=1, obstacle_type=Lava, **kwargs): self.num_crossings = num_crossings self.obstacle_type = obstacle_type super().__init__( grid_size=size, max_steps=4 * size * size, # Set this to True for maximum speed see_through_walls=False, **kwargs ) def _gen_grid(self, width, height): assert width % 2 == 1 and height % 2 == 1 # odd size # Create an empty grid self.grid = Grid(width, height) # Generate the surrounding walls self.grid.wall_rect(0, 0, width, height) # Place the agent in the top-left corner self.agent_pos = np.array((1, 1)) self.agent_dir = 0 # Place a goal square in the bottom-right corner self.put_obj(Goal(), width - 2, height - 2) # Place obstacles (lava or walls) v, h = object(), object() # singleton `vertical` and `horizontal` objects # Lava rivers or walls specified by direction and position in grid rivers = [(v, i) for i in range(2, height - 2, 2)] rivers += [(h, j) for j in range(2, width - 2, 2)] self.np_random.shuffle(rivers) rivers = rivers[: self.num_crossings] # sample random rivers rivers_v = sorted(pos for direction, pos in rivers if direction is v) rivers_h = sorted(pos for direction, pos in rivers if direction is h) obstacle_pos = itt.chain( itt.product(range(1, width - 1), rivers_h), itt.product(rivers_v, range(1, height - 1)), ) for i, j in obstacle_pos: self.put_obj(self.obstacle_type(), i, j) # Sample path to goal path = [h] * len(rivers_v) + [v] * len(rivers_h) self.np_random.shuffle(path) # Create openings limits_v = [0] + rivers_v + [height - 1] limits_h = [0] + rivers_h + [width - 1] room_i, room_j = 0, 0 for direction in path: if direction is h: i = limits_v[room_i + 1] j = self.np_random.choice( range(limits_h[room_j] + 1, limits_h[room_j + 1]) ) room_i += 1 elif direction is v: i = self.np_random.choice( range(limits_v[room_i] + 1, limits_v[room_i + 1]) ) j = limits_h[room_j + 1] room_j += 1 else: assert False self.grid.set(i, j, None) self.mission = ( "avoid the lava and get to the green goal square" if self.obstacle_type == Lava else "find the opening and get to the green goal square" ) register( id="MiniGrid-LavaCrossingS9N1-v0", entry_point="gym_minigrid.envs.crossing:CrossingEnv", size=9, num_crossings=1, ) register( id="MiniGrid-LavaCrossingS9N2-v0", entry_point="gym_minigrid.envs.crossing:CrossingEnv", size=9, num_crossings=2, ) register( id="MiniGrid-LavaCrossingS9N3-v0", entry_point="gym_minigrid.envs.crossing:CrossingEnv", size=9, num_crossings=3, ) register( id="MiniGrid-LavaCrossingS11N5-v0", entry_point="gym_minigrid.envs.crossing:CrossingEnv", size=11, num_crossings=5, ) register( id="MiniGrid-SimpleCrossingS9N1-v0", entry_point="gym_minigrid.envs.crossing:CrossingEnv", size=9, num_crossings=1, obstacle_type=Wall, ) register( id="MiniGrid-SimpleCrossingS9N2-v0", entry_point="gym_minigrid.envs.crossing:CrossingEnv", size=9, num_crossings=2, obstacle_type=Wall, ) register( id="MiniGrid-SimpleCrossingS9N3-v0", entry_point="gym_minigrid.envs.crossing:CrossingEnv", size=9, num_crossings=3, obstacle_type=Wall, ) register( id="MiniGrid-SimpleCrossingS11N5-v0", entry_point="gym_minigrid.envs.crossing:CrossingEnv", size=11, num_crossings=5, obstacle_type=Wall, )