You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
128 lines
3.8 KiB
128 lines
3.8 KiB
from __future__ import annotations
|
|
|
|
from minigrid.core.grid import Grid
|
|
from minigrid.core.mission import MissionSpace
|
|
from minigrid.core.world_object import Goal
|
|
from minigrid.minigrid_env import MiniGridEnv
|
|
|
|
|
|
class FourRoomsEnv(MiniGridEnv):
|
|
|
|
"""
|
|
## Description
|
|
|
|
Classic four room reinforcement learning environment. The agent must
|
|
navigate in a maze composed of four rooms interconnected by 4 gaps in the
|
|
walls. To obtain a reward, the agent must reach the green goal square. Both
|
|
the agent and the goal square are randomly placed in any of the four rooms.
|
|
|
|
## Mission Space
|
|
|
|
"reach the goal"
|
|
|
|
## 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 | Unused |
|
|
|
|
## 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/minigrid.py](minigrid/minigrid.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 reaches the goal.
|
|
2. Timeout (see `max_steps`).
|
|
|
|
## Registered Configurations
|
|
|
|
- `MiniGrid-FourRooms-v0`
|
|
|
|
"""
|
|
|
|
def __init__(self, agent_pos=None, goal_pos=None, max_steps=100, **kwargs):
|
|
self._agent_default_pos = agent_pos
|
|
self._goal_default_pos = goal_pos
|
|
|
|
self.size = 19
|
|
mission_space = MissionSpace(mission_func=self._gen_mission)
|
|
|
|
super().__init__(
|
|
mission_space=mission_space,
|
|
width=self.size,
|
|
height=self.size,
|
|
max_steps=max_steps,
|
|
**kwargs,
|
|
)
|
|
|
|
@staticmethod
|
|
def _gen_mission():
|
|
return "reach the goal"
|
|
|
|
def _gen_grid(self, width, height):
|
|
# Create the grid
|
|
self.grid = Grid(width, height)
|
|
|
|
# Generate the surrounding walls
|
|
self.grid.horz_wall(0, 0)
|
|
self.grid.horz_wall(0, height - 1)
|
|
self.grid.vert_wall(0, 0)
|
|
self.grid.vert_wall(width - 1, 0)
|
|
|
|
room_w = width // 2
|
|
room_h = height // 2
|
|
|
|
# For each row of rooms
|
|
for j in range(0, 2):
|
|
|
|
# For each column
|
|
for i in range(0, 2):
|
|
xL = i * room_w
|
|
yT = j * room_h
|
|
xR = xL + room_w
|
|
yB = yT + room_h
|
|
|
|
# Bottom wall and door
|
|
if i + 1 < 2:
|
|
self.grid.vert_wall(xR, yT, room_h)
|
|
pos = (xR, self._rand_int(yT + 1, yB))
|
|
self.grid.set(*pos, None)
|
|
|
|
# Bottom wall and door
|
|
if j + 1 < 2:
|
|
self.grid.horz_wall(xL, yB, room_w)
|
|
pos = (self._rand_int(xL + 1, xR), yB)
|
|
self.grid.set(*pos, None)
|
|
|
|
# Randomize the player start position and orientation
|
|
if self._agent_default_pos is not None:
|
|
self.agent_pos = self._agent_default_pos
|
|
self.grid.set(*self._agent_default_pos, None)
|
|
# assuming random start direction
|
|
self.agent_dir = self._rand_int(0, 4)
|
|
else:
|
|
self.place_agent()
|
|
|
|
if self._goal_default_pos is not None:
|
|
goal = Goal()
|
|
self.put_obj(goal, *self._goal_default_pos)
|
|
goal.init_pos, goal.cur_pos = self._goal_default_pos
|
|
else:
|
|
self.place_obj(Goal())
|