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maze.py
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maze.py
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# coding=utf-8
# Copyright 2021 The Google Research Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import gym_minigrid.minigrid as minigrid
import numpy as np
from . import multigrid
# from . import register
import envs.registration as register
class MazeEnv(multigrid.MultiGridEnv):
"""Single-agent maze environment specified via a bit map."""
def __init__(self, agent_view_size=5, minigrid_mode=True, max_steps=None,
bit_map=None, start_pos=None, goal_pos=None, size=15):
default_agent_start_x = 7
default_agent_start_y = 1
default_goal_start_x = 7
default_goal_start_y = 13
self.start_pos = np.array(
[default_agent_start_x,
default_agent_start_y]) if start_pos is None else start_pos
self.goal_pos = (
default_goal_start_x,
default_goal_start_y) if goal_pos is None else goal_pos
if max_steps is None:
max_steps = 2*size*size
if bit_map is not None:
bit_map = np.array(bit_map)
if bit_map.shape != (size-2, size-2):
print('Error! Bit map shape does not match size. Using default maze.')
bit_map = None
if bit_map is None:
self.bit_map = np.array([
[0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0],
[0, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 0],
[0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1],
[0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0],
[1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0],
[0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0],
[0, 1, 1, 0, 0, 0, 1, 1, 0, 1, 1, 1, 1],
[0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 0, 1, 0],
[1, 0, 1, 0, 1, 1, 0, 1, 1, 1, 0, 1, 0],
[1, 0, 1, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0],
[1, 0, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 0],
[0, 0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 0]
])
else:
self.bit_map = bit_map
super().__init__(
n_agents=1,
grid_size=size,
agent_view_size=agent_view_size,
max_steps=max_steps,
see_through_walls=True, # Set this to True for maximum speed
minigrid_mode=minigrid_mode
)
def _gen_grid(self, width, height):
# Create an empty grid
self.grid = multigrid.Grid(width, height)
# Generate the surrounding walls
self.grid.wall_rect(0, 0, width, height)
# Goal
self.put_obj(minigrid.Goal(), self.goal_pos[0], self.goal_pos[1])
# Agent
self.place_agent_at_pos(0, self.start_pos)
# Walls
for x in range(self.bit_map.shape[0]):
for y in range(self.bit_map.shape[1]):
if self.bit_map[y, x]:
# Add an offset of 1 for the outer walls
self.put_obj(minigrid.Wall(), x+1, y+1)
class HorizontalMazeEnv(MazeEnv):
"""A short but non-optimal path is 80 moves."""
def __init__(self):
# positions go col, row
start_pos = np.array([1, 7])
goal_pos = np.array([13, 5])
bit_map = np.array([
[0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 1, 0, 0],
[0, 1, 0, 1, 0, 1, 1, 1, 1, 0, 0, 0, 1],
[0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0],
[0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1],
[0, 0, 0, 1, 0, 0, 1, 0, 1, 0, 1, 0, 0],
[1, 1, 0, 1, 0, 1, 1, 0, 1, 0, 1, 0, 0],
[0, 1, 0, 1, 0, 0, 0, 0, 1, 0, 1, 1, 0],
[0, 1, 0, 1, 1, 0, 1, 1, 1, 0, 0, 1, 0],
[0, 1, 0, 0, 1, 0, 0, 1, 1, 1, 0, 1, 0],
[0, 1, 1, 0, 1, 1, 0, 1, 0, 1, 0, 1, 0],
[0, 1, 0, 0, 0, 1, 0, 1, 0, 1, 0, 1, 0],
[0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0],
[0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0]
])
super().__init__(size=15, bit_map=bit_map, start_pos=start_pos,
goal_pos=goal_pos)
class Maze3Env(MazeEnv):
"""A short but non-optimal path is 80 moves."""
def __init__(self):
# positions go col, row and indexing starts at 1
start_pos = np.array([4, 1])
goal_pos = np.array([13, 7])
bit_map = np.array([
[0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0],
[0, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 0],
[0, 1, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0],
[0, 0, 0, 1, 1, 1, 1, 0, 1, 0, 1, 0, 1],
[1, 1, 0, 1, 0, 0, 0, 0, 1, 0, 1, 0, 0],
[0, 0, 0, 1, 0, 1, 1, 0, 1, 0, 1, 1, 0],
[0, 1, 0, 1, 0, 1, 0, 0, 1, 0, 0, 1, 0],
[0, 1, 0, 1, 0, 1, 0, 1, 1, 1, 0, 1, 1],
[0, 1, 0, 0, 0, 1, 0, 1, 0, 1, 0, 0, 0],
[0, 1, 1, 1, 0, 1, 0, 1, 0, 1, 1, 1, 0],
[0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0],
[0, 1, 0, 1, 1, 1, 0, 1, 0, 1, 0, 1, 0],
[0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0]
])
super().__init__(size=15, bit_map=bit_map, start_pos=start_pos,
goal_pos=goal_pos)
class SmallCorridorEnv(MazeEnv):
"""A shorter backtracking env."""
def __init__(self):
# positions go col, row and indexing starts at 1
start_pos = np.array([1, 7])
row = np.random.choice([6,8])
col = np.random.choice([3,5,7,9,11])
goal_pos = np.array([col,row])
bit_map = np.array([
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0],
[0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0],
[0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0],
[0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0],
[0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0],
[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0],
[0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0],
[0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0],
[0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0],
[0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0],
[0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
])
super().__init__(size=15, bit_map=bit_map, start_pos=start_pos,
goal_pos=goal_pos)
class LargeCorridorEnv(MazeEnv):
"""A long backtracking env."""
def __init__(self):
# positions go col, row and indexing starts at 1
start_pos = np.array([1, 10])
row = np.random.choice([9, 11])
col = np.random.choice([3,5,7,9,11,13,15,17])
goal_pos = np.array([col,row])
bit_map = np.array([
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0],
[0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0],
[0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0],
[0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0],
[0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0],
[0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0],
[0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0],
[0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0],
[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0],
[0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0],
[0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0],
[0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0],
[0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0],
[0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0],
[0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0],
[0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0],
[0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
])
super().__init__(size=21, bit_map=bit_map, start_pos=start_pos,
goal_pos=goal_pos)
class LabyrinthEnv(MazeEnv):
"""A short but non-optimal path is 118 moves."""
def __init__(self):
# positions go col, row
start_pos = np.array([1, 13])
goal_pos = np.array([7, 7])
bit_map = np.array([
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0],
[0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0],
[0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0],
[0, 1, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 0],
[0, 1, 0, 1, 0, 1, 1, 1, 0, 1, 0, 1, 0],
[0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0],
[0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0],
[0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 1, 0],
[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0],
[0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0],
[1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 0],
[0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0]
])
super().__init__(size=15, bit_map=bit_map, start_pos=start_pos,
goal_pos=goal_pos)
class Labyrinth2Env(MazeEnv):
"""A short but non-optimal path is 118 moves."""
def __init__(self):
# positions go col, row
start_pos = np.array([1, 1])
goal_pos = np.array([7, 7])
bit_map = np.array([
[0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0],
[0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0],
[0, 1, 0, 1, 0, 1, 1, 1, 1, 1, 0, 1, 0],
[0, 1, 0, 1, 0, 1, 0, 0, 0, 1, 0, 1, 0],
[0, 0, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0],
[1, 1, 1, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0],
[0, 0, 0, 1, 0, 1, 1, 1, 0, 1, 0, 1, 0],
[0, 1, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 0],
[0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0],
[0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0],
[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
])
super().__init__(size=15, bit_map=bit_map, start_pos=start_pos,
goal_pos=goal_pos)
class NineRoomsEnv(MazeEnv):
"""Can be completed in 27 moves."""
def __init__(self):
# positions go col, row
start_pos = np.array([2, 2])
goal_pos = np.array([12, 12])
bit_map = np.array([
[0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0],
[0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0],
[0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0],
[1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0],
[0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0],
[0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0],
[1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 0, 1, 1],
[0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0],
[0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0]
])
super().__init__(size=15, bit_map=bit_map, start_pos=start_pos,
goal_pos=goal_pos)
class NineRoomsFewerDoorsEnv(MazeEnv):
"""Can be completed in 27 moves."""
def __init__(self):
# positions go col, row
start_pos = np.array([2, 2])
goal_pos = np.array([12, 12])
bit_map = np.array([
[0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0],
[0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0],
[0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0],
[1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0],
[0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0],
[0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0],
[1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 0, 1, 1],
[0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0],
[0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0],
[0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0]
])
super().__init__(size=15, bit_map=bit_map, start_pos=start_pos,
goal_pos=goal_pos)
class SixteenRoomsEnv(MazeEnv):
"""Can be completed in 16 moves."""
def __init__(self):
# positions go col, row
start_pos = np.array([2, 2])
goal_pos = np.array([12, 12])
bit_map = np.array([
[0, 0, 0, 1, 0, 0, 1, 0, 0, 1, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0],
[0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0],
[1, 0, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 0],
[0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0],
[1, 1, 0, 1, 0, 1, 1, 0, 1, 1, 1, 0, 1],
[0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0],
[0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0],
[0, 1, 1, 1, 1, 0, 1, 1, 0, 1, 0, 1, 1],
[0, 0, 0, 1, 0, 0, 1, 0, 0, 1, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0]
])
super().__init__(size=15, bit_map=bit_map, start_pos=start_pos,
goal_pos=goal_pos)
class SixteenRoomsFewerDoorsEnv(MazeEnv):
"""Can be completed in 16 moves."""
def __init__(self):
# positions go col, row
start_pos = np.array([2, 2])
goal_pos = np.array([12, 12])
bit_map = np.array([
[0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0],
[0, 0, 0, 1, 0, 0, 1, 0, 0, 1, 0, 0, 0],
[1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 0, 1],
[0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0],
[1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1],
[0, 0, 0, 1, 0, 0, 1, 0, 0, 1, 0, 0, 0],
[0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1],
[0, 0, 0, 1, 0, 0, 1, 0, 0, 1, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 1, 0, 0, 1, 0, 0, 1, 0, 0, 0]
])
super().__init__(size=15, bit_map=bit_map, start_pos=start_pos,
goal_pos=goal_pos)
class MiniMazeEnv(MazeEnv):
"""A smaller maze for debugging."""
def __init__(self):
start_pos = np.array([1, 1])
goal_pos = np.array([1, 3])
bit_map = np.array([
[0, 0, 0, 0],
[1, 1, 1, 0],
[0, 1, 0, 0],
[0, 0, 0, 1],
])
super().__init__(size=6, bit_map=bit_map, start_pos=start_pos,
goal_pos=goal_pos)
class MediumMazeEnv(MazeEnv):
"""A 10x10 Maze environment."""
def __init__(self):
start_pos = np.array([5, 1])
goal_pos = np.array([3, 8])
bit_map = np.array([
[0, 1, 0, 0, 0, 1, 1, 0],
[0, 1, 0, 1, 0, 1, 0, 0],
[0, 1, 0, 1, 1, 1, 1, 0],
[0, 0, 0, 0, 0, 1, 0, 0],
[1, 1, 1, 1, 0, 1, 0, 1],
[0, 0, 0, 0, 0, 0, 0, 0],
[0, 1, 1, 1, 1, 1, 1, 0],
[0, 0, 0, 1, 0, 0, 0, 0],
])
super().__init__(size=10, bit_map=bit_map, start_pos=start_pos,
goal_pos=goal_pos)
if hasattr(__loader__, 'name'):
module_path = __loader__.name
elif hasattr(__loader__, 'fullname'):
module_path = __loader__.fullname
register.register(
id='MultiGrid-Maze-v0',
entry_point=module_path + ':MazeEnv'
)
register.register(
id='MultiGrid-MiniMaze-v0',
entry_point=module_path + ':MiniMazeEnv'
)
register.register(
id='MultiGrid-MediumMaze-v0',
entry_point=module_path + ':MediumMazeEnv'
)
register.register(
id='MultiGrid-Maze2-v0',
entry_point=module_path + ':HorizontalMazeEnv'
)
register.register(
id='MultiGrid-Maze3-v0',
entry_point=module_path + ':Maze3Env'
)
register.register(
id='MultiGrid-SmallCorridor-v0',
entry_point=module_path + ':SmallCorridorEnv'
)
register.register(
id='MultiGrid-LargeCorridor-v0',
entry_point=module_path + ':LargeCorridorEnv'
)
register.register(
id='MultiGrid-Labyrinth-v0',
entry_point=module_path + ':LabyrinthEnv'
)
register.register(
id='MultiGrid-Labyrinth2-v0',
entry_point=module_path + ':Labyrinth2Env'
)
register.register(
id='MultiGrid-SixteenRooms-v0',
entry_point=module_path + ':SixteenRoomsEnv'
)
register.register(
id='MultiGrid-SixteenRoomsFewerDoors-v0',
entry_point=module_path + ':SixteenRoomsFewerDoorsEnv'
)
register.register(
id='MultiGrid-NineRooms-v0',
entry_point=module_path + ':NineRoomsEnv'
)
register.register(
id='MultiGrid-NineRoomsFewerDoors-v0',
entry_point=module_path + ':NineRoomsFewerDoorsEnv'
)