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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.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"
)
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'
)