zuoxingdong / mazelab Goto Github PK

Hi,

I was running through the examples you have provided and attempted to render the the empty maze environment. However, the resulting image is oddly blurry as attached. Would you know the reason as to why and if there are any fixes?

Thank you!

Hi,

May I check if it is possible to create an environment supporting multi-agent exploration? Thank you!

Following the jupyter notebook, I'm getting the above error when running the line:
from mazelab import BaseMaze

TypeError Traceback (most recent call last)
in
1 import numpy as np
----> 2 from mazelab import BaseMaze
3 from mazelab import Object
4 from mazelab import DeepMindColor as color
5

~/anaconda3/envs/slime/mazelab/mazelab/init.py in
3 from .object import Object
4
----> 5 from .motion import VonNeumannMotion
6 from .motion import MooreMotion
7

~/anaconda3/envs/slime/mazelab/mazelab/motion.py in
4 VonNeumannMotion = namedtuple('VonNeumannMotion',
5 ['north', 'south', 'west', 'east'],
----> 6 defaults=[[-1, 0], [1, 0], [0, -1], [0, 1]])
7
8

TypeError: namedtuple() got an unexpected keyword argument 'defaults'

It seems that negative or out of edge position will result index error in _is_valid() method, because of the passable check.
I think it should be changed to
def _is_valid(self, position):
nonnegative = position[0] >= 0 and position[1] >= 0
within_edge = position[0] < self.maze.size[0] and position[1] < self.maze.size[1]
if (nonnegative and within_edge):
passable = not self.maze.to_impassable()[position[0]][position[1]]
else:
passable = False
return nonnegative and within_edge and passable

IndexError Traceback (most recent call last)
in
35 action = agent.act(state, epsilon)
36
---> 37 _, reward, done, _ = env.step(action)
38 next_state = env.unwrapped.maze.objects.agent.positions[0]
39

//anaconda3/lib/python3.7/site-packages/gym/wrappers/time_limit.py in step(self, action)
14 def step(self, action):
15 assert self._elapsed_steps is not None, "Cannot call env.step() before calling reset()"
---> 16 observation, reward, done, info = self.env.step(action)
17 self._elapsed_steps += 1
18 if self._elapsed_steps >= self._max_episode_steps:

in step(self, action)
21 current_position = self.maze.objects.agent.positions[0]
22 new_position = [current_position[0] + motion[0], current_position[1] + motion[1]]
---> 23 valid = self._is_valid(new_position)
24 if valid:
25 self.maze.objects.agent.positions = [new_position]

in _is_valid(self, position)
44 nonnegative = position[0] >= 0 and position[1] >= 0
45 within_edge = position[0] < self.maze.size[0] and position[1] < self.maze.size[1]
---> 46 passable = not self.maze.to_impassable()[position[0]][position[1]]
47 return nonnegative and within_edge and passable
48

IndexError: index 6 is out of bounds for axis 0 with size 6

when running

from gym_maze.envs import MazeEnv
from gym_maze.envs.generators import *
maze = RandomBlockMazeGenerator(maze_size=4, obstacle_ratio=0.0)
env = MazeEnv(maze)
env.reset()

I get the following error:

Traceback (most recent call last):
WARN: gym.spaces.Box autodetected dtype as <class 'numpy.float32'>. Please provide explicit dtype.  File "C:/Users/Yitzhak/Projects/gym-maze/example_1.py", line 5, in <module>
    env.reset()
  File "C:\Users\Yitzhak\Anaconda3\lib\site-packages\gym\core.py", line 71, in reset
    raise NotImplementedError
NotImplementedError

Support metadata: reward_range, spec etc

• Use ImageViewer from lagom to display observation, do not use saving list of arrays, it is too slow.
• Synchronize some tricks from lagom
• Redesign classes

Thanks for releasing this project. Could you add License?