from gym_minigrid.minigrid import * from gym_minigrid.register import register import itertools as itt class CrossingEnv(MiniGridEnv): """ Environment with wall or lava obstacles, sparse reward. """ def __init__(self, size=9, num_crossings=1, obstacle_type=Lava, seed=None): 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, seed=None ) 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 = (1, 1) self.agent_dir = 0 # Place a goal square in the bottom-right corner self.grid.set(width - 2, height - 2, Goal()) # 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.grid.set(i, j, self.obstacle_type()) # 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" ) class LavaCrossingEnv(CrossingEnv): def __init__(self): super().__init__(size=9, num_crossings=1) class LavaCrossingS9N2Env(CrossingEnv): def __init__(self): super().__init__(size=9, num_crossings=2) class LavaCrossingS9N3Env(CrossingEnv): def __init__(self): super().__init__(size=9, num_crossings=3) class LavaCrossingS11N5Env(CrossingEnv): def __init__(self): super().__init__(size=11, num_crossings=5) register( id='MiniGrid-LavaCrossingS9N1-v0', entry_point='gym_minigrid.envs:LavaCrossingEnv' ) register( id='MiniGrid-LavaCrossingS9N2-v0', entry_point='gym_minigrid.envs:LavaCrossingS9N2Env' ) register( id='MiniGrid-LavaCrossingS9N3-v0', entry_point='gym_minigrid.envs:LavaCrossingS9N3Env' ) register( id='MiniGrid-LavaCrossingS11N5-v0', entry_point='gym_minigrid.envs:LavaCrossingS11N5Env' ) class SimpleCrossingEnv(CrossingEnv): def __init__(self): super().__init__(size=9, num_crossings=1, obstacle_type=Wall) class SimpleCrossingS9N2Env(CrossingEnv): def __init__(self): super().__init__(size=9, num_crossings=2, obstacle_type=Wall) class SimpleCrossingS9N3Env(CrossingEnv): def __init__(self): super().__init__(size=9, num_crossings=3, obstacle_type=Wall) class SimpleCrossingS11N5Env(CrossingEnv): def __init__(self): super().__init__(size=11, num_crossings=5, obstacle_type=Wall) register( id='MiniGrid-SimpleCrossingS9N1-v0', entry_point='gym_minigrid.envs:SimpleCrossingEnv' ) register( id='MiniGrid-SimpleCrossingS9N2-v0', entry_point='gym_minigrid.envs:SimpleCrossingS9N2Env' ) register( id='MiniGrid-SimpleCrossingS9N3-v0', entry_point='gym_minigrid.envs:SimpleCrossingS9N3Env' ) register( id='MiniGrid-SimpleCrossingS11N5-v0', entry_point='gym_minigrid.envs:SimpleCrossingS11N5Env' )