The source code and dockerfile for the GSW2024 AI Lab.
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from __future__ import annotations
from minigrid.core.grid import Grid
from minigrid.core.mission import MissionSpace
from minigrid.core.world_object import Goal, Lava, SlipperyNorth, SlipperyEast, SlipperySouth, SlipperyWest, Ball
from minigrid.envs.adversaries_base import AdversaryEnv
from minigrid.core.tasks import GoTo, DoNothing, PickUpObject, PlaceObject
import numpy as np
class AdversarySimple(AdversaryEnv):
"""
## Description
## Registered Configurations
- `MiniGrid-Adv-8x8-v0`
- `MiniGrid-AdvLava-8x8-v0`
- `MiniGrid-AdvSlipperyLava-8x8-v0`
- `MiniGrid-AdvSimple-8x8-v0`
"""
def __init__(self, width=7, height=6, generate_wall=True, generate_lava=False, generate_slippery=False ,max_steps: int | None = None, **kwargs):
if max_steps is None:
max_steps = 200
self.generate_wall = generate_wall
super().__init__(
width=width, height=height, max_steps=max_steps, **kwargs
)
def _gen_grid(self, width, height):
self.grid = Grid(width, height)
self.grid.wall_rect(0, 0, width, height)
goal_pos = np.array((width - 2, height - 2))
ball_pos = np.array((width - 3, height - 2))
self.put_obj(Goal(), *goal_pos)
self.put_obj(Ball("yellow"), *ball_pos)
ball = self.grid.get(*ball_pos)
self.adversaries = {}
self.agent_pos = np.array((width - 2, 1))
self.agent_dir = 2
self.grid.horz_wall(2, height - 3)
blue_adv = self.add_adversary(1, 1, "yellow", direction=1, tasks=[GoTo((width - 4, height - 2)),
PickUpObject(ball_pos, ball),
GoTo((1,1)),
PlaceObject((2, 1), ball),
DoNothing(duration=2),
PickUpObject((2, 1), ball),
GoTo((width - 4, height - 2)),
PlaceObject(ball_pos, ball)], repeating=True)
def step(self, action):
obs, reward, terminated, truncated, info = super().step(action)
return obs, reward, terminated, truncated, info