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139 lines
4.0 KiB
139 lines
4.0 KiB
from __future__ import annotations
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import numpy as np
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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 Goal, Lava
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from minigrid.minigrid_env import MiniGridEnv
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class LavaGapEnv(MiniGridEnv):
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"""
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## Description
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The agent has to reach the green goal square at the opposite corner of the
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room, and must pass through a narrow gap in a vertical strip of deadly lava.
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Touching the lava terminate the episode with a zero reward. This environment
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is useful for studying safety and safe exploration.
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## Mission Space
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Depending on the `obstacle_type` parameter:
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- `Lava`: "avoid the lava and get to the green goal square"
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- otherwise: "find the opening and get to the green goal square"
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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 | Unused |
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| 4 | drop | Unused |
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| 5 | toggle | Unused |
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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. The agent falls into lava.
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3. Timeout (see `max_steps`).
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## Registered Configurations
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S: size of map SxS.
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- `MiniGrid-LavaGapS5-v0`
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- `MiniGrid-LavaGapS6-v0`
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- `MiniGrid-LavaGapS7-v0`
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"""
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def __init__(
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self, size, obstacle_type=Lava, max_steps: int | None = None, **kwargs
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):
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self.obstacle_type = obstacle_type
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self.size = size
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if obstacle_type == Lava:
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mission_space = MissionSpace(mission_func=self._gen_mission_lava)
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else:
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mission_space = MissionSpace(mission_func=self._gen_mission)
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if max_steps is None:
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max_steps = 4 * size**2
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super().__init__(
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mission_space=mission_space,
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width=size,
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height=size,
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# Set this to True for maximum speed
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see_through_walls=False,
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max_steps=max_steps,
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**kwargs,
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)
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@staticmethod
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def _gen_mission_lava():
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return "avoid the lava and get to the green goal square"
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@staticmethod
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def _gen_mission():
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return "find the opening and get to the green goal square"
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def _gen_grid(self, width, height):
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assert width >= 5 and height >= 5
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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 the agent in the top-left corner
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self.agent_pos = np.array((1, 1))
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self.agent_dir = 0
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# Place a goal square in the bottom-right corner
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self.goal_pos = np.array((width - 2, height - 2))
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self.put_obj(Goal(), *self.goal_pos)
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# Generate and store random gap position
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self.gap_pos = np.array(
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(
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#self._rand_int(2, width - 2),
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#self._rand_int(1, height - 1),
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self._rand_int(2,3),
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self._rand_int(2,3),
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)
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)
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# Place the obstacle wall
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self.grid.vert_wall(self.gap_pos[0], 1, height - 2, self.obstacle_type)
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# Put a hole in the wall
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self.grid.set(*self.gap_pos, None)
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self.mission = (
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"avoid the lava and get to the green goal square"
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if self.obstacle_type == Lava
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else "find the opening and get to the green goal square"
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)
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