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- from __future__ import annotations
- from minigrid.core.constants import COLOR_NAMES
- from minigrid.core.grid import Grid
- from minigrid.core.mission import MissionSpace
- from minigrid.core.world_object import Door
- from minigrid.minigrid_env import MiniGridEnv
- class GoToDoorEnv(MiniGridEnv):
- """
- ## Description
- This environment is a room with four doors, one on each wall. The agent
- receives a textual (mission) string as input, telling it which door to go
- to, (eg: "go to the red door"). It receives a positive reward for performing
- the `done` action next to the correct door, as indicated in the mission
- string.
- ## Mission Space
- "go to the {color} door"
- {color} is the color of the door. Can be "red", "green", "blue", "purple",
- "yellow" or "grey".
- ## Action Space
- | Num | Name | Action |
- |-----|--------------|----------------------|
- | 0 | left | Turn left |
- | 1 | right | Turn right |
- | 2 | forward | Move forward |
- | 3 | pickup | Unused |
- | 4 | drop | Unused |
- | 5 | toggle | Unused |
- | 6 | done | Done completing task |
- ## Observation Encoding
- - Each tile is encoded as a 3 dimensional tuple:
- `(OBJECT_IDX, COLOR_IDX, STATE)`
- - `OBJECT_TO_IDX` and `COLOR_TO_IDX` mapping can be found in
- [minigrid/core/constants.py](minigrid/core/constants.py)
- - `STATE` refers to the door state with 0=open, 1=closed and 2=locked
- ## Rewards
- A reward of '1 - 0.9 * (step_count / max_steps)' is given for success, and '0' for failure.
- ## Termination
- The episode ends if any one of the following conditions is met:
- 1. The agent stands next the correct door performing the `done` action.
- 2. Timeout (see `max_steps`).
- ## Registered Configurations
- - `MiniGrid-GoToDoor-5x5-v0`
- - `MiniGrid-GoToDoor-6x6-v0`
- - `MiniGrid-GoToDoor-8x8-v0`
- """
- def __init__(self, size=5, max_steps: int | None = None, **kwargs):
- assert size >= 5
- self.size = size
- mission_space = MissionSpace(
- mission_func=self._gen_mission,
- ordered_placeholders=[COLOR_NAMES],
- )
- if max_steps is None:
- max_steps = 4 * size**2
- super().__init__(
- mission_space=mission_space,
- width=size,
- height=size,
- # Set this to True for maximum speed
- see_through_walls=True,
- max_steps=max_steps,
- **kwargs,
- )
- @staticmethod
- def _gen_mission(color: str):
- return f"go to the {color} door"
- def _gen_grid(self, width, height):
- # Create the grid
- self.grid = Grid(width, height)
- # Randomly vary the room width and height
- width = self._rand_int(5, width + 1)
- height = self._rand_int(5, height + 1)
- # Generate the surrounding walls
- self.grid.wall_rect(0, 0, width, height)
- # Generate the 4 doors at random positions
- doorPos = []
- doorPos.append((self._rand_int(2, width - 2), 0))
- doorPos.append((self._rand_int(2, width - 2), height - 1))
- doorPos.append((0, self._rand_int(2, height - 2)))
- doorPos.append((width - 1, self._rand_int(2, height - 2)))
- # Generate the door colors
- doorColors = []
- while len(doorColors) < len(doorPos):
- color = self._rand_elem(COLOR_NAMES)
- if color in doorColors:
- continue
- doorColors.append(color)
- # Place the doors in the grid
- for idx, pos in enumerate(doorPos):
- color = doorColors[idx]
- self.grid.set(*pos, Door(color))
- # Randomize the agent start position and orientation
- self.place_agent(size=(width, height))
- # Select a random target door
- doorIdx = self._rand_int(0, len(doorPos))
- self.target_pos = doorPos[doorIdx]
- self.target_color = doorColors[doorIdx]
- # Generate the mission string
- self.mission = "go to the %s door" % self.target_color
- def step(self, action):
- obs, reward, terminated, truncated, info = super().step(action)
- ax, ay = self.agent_pos
- tx, ty = self.target_pos
- # Don't let the agent open any of the doors
- if action == self.actions.toggle:
- terminated = True
- # Reward performing done action in front of the target door
- if action == self.actions.done:
- if (ax == tx and abs(ay - ty) == 1) or (ay == ty and abs(ax - tx) == 1):
- reward = self._reward()
- terminated = True
- return obs, reward, terminated, truncated, info
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