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109 lines
3.7 KiB
109 lines
3.7 KiB
from __future__ import annotations
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from minigrid.core.grid import Grid
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from minigrid.core.mission import MissionSpace
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from minigrid.core.world_object import Door, Goal, Key
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from minigrid.minigrid_env import MiniGridEnv
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class DoubleDoorEnv(MiniGridEnv):
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"""
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## Description
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This environment has a key that the agent must pick up in order to unlock a
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goal and then get to the green goal square. This environment is difficult,
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because of the sparse reward, to solve using classical RL algorithms. It is
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useful to experiment with curiosity or curriculum learning.
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## Mission Space
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"use the key to open the door and then get to the goal"
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## Action Space
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| Num | Name | Action |
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|-----|--------------|---------------------------|
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| 0 | left | Turn left |
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| 1 | right | Turn right |
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| 2 | forward | Move forward |
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| 3 | pickup | Pick up an object |
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| 4 | drop | Unused |
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| 5 | toggle | Toggle/activate an object |
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| 6 | done | Unused |
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## Observation Encoding
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- Each tile is encoded as a 3 dimensional tuple:
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`(OBJECT_IDX, COLOR_IDX, STATE)`
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- `OBJECT_TO_IDX` and `COLOR_TO_IDX` mapping can be found in
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[minigrid/minigrid.py](minigrid/minigrid.py)
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- `STATE` refers to the door state with 0=open, 1=closed and 2=locked
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## Rewards
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A reward of '1 - 0.9 * (step_count / max_steps)' is given for success, and '0' for failure.
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## Termination
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The episode ends if any one of the following conditions is met:
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1. The agent reaches the goal.
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2. Timeout (see `max_steps`).
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## Registered Configurations
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- `MiniGrid-DoubleDoor-16x16-v0`
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- `MiniGrid-DoubleDoor-12x12-v0`
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- `MiniGrid-DoubleDoor-10x8-v0`
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"""
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def __init__(self, width=8, height=8, max_steps: int | None = None, **kwargs):
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if max_steps is None:
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max_steps = 10 * (width * height)
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mission_space = MissionSpace(mission_func=self._gen_mission)
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super().__init__(
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mission_space=mission_space, width=width, height=height, max_steps=max_steps, **kwargs
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)
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@staticmethod
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def _gen_mission():
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return "use the key to open the door and then get to the goal"
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def _gen_grid(self, width, height):
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# Create an empty grid
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self.grid = Grid(width, height)
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# Generate the surrounding walls
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self.grid.wall_rect(0, 0, width, height)
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# Place a goal in the bottom-right corner
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self.put_obj(Goal(), width - 2, height - 2)
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# Create first vertical splitting wall
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# splitIdx = self._rand_int(2, width - 2)
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splitIdxOne = width // 3
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self.grid.vert_wall(splitIdxOne, 0)
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# Place the agent at a random position and orientation
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# on the left side of the splitting wall
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self.place_agent(size=(splitIdxOne, height))
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# Place a door in the wall
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doorIdx = height // 2
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self.put_obj(Door("yellow", is_locked=True), splitIdxOne, doorIdx)
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self.place_obj(obj=Key("yellow"), top=(0, 0), size=(splitIdxOne, height))
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# Create second vertical splitting wall
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splitIdxTwo = (width // 3) * 2
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self.grid.vert_wall(splitIdxTwo, 0)
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doorIdx = height // 2
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self.put_obj(Door("red", is_locked=True), splitIdxTwo, doorIdx)
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self.place_obj(obj=Key("red"), top=(splitIdxOne, 0), size=(splitIdxTwo - splitIdxOne, height))
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# Place a yellow key on the left side
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self.mission = "use the key to open the door and then get to the goal"
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