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 Ball, Box, Key from minigrid.minigrid_env import MiniGridEnv class GoToObjectEnv(MiniGridEnv): """ ## Description This environment is a room with colored objects. The agent receives a textual (mission) string as input, telling it which colored object to go to, (eg: "go to the red key"). It receives a positive reward for performing the `done` action next to the correct object, as indicated in the mission string. ## Mission Space "go to the {color} {obj_type}" {color} is the color of the object. Can be "red", "green", "blue", "purple", "yellow" or "grey". {obj_type} is the type of the object. Can be "key", "ball", "box". ## 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/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 stands next the correct door performing the `done` action. 2. Timeout (see `max_steps`). ## Registered Configurations - `MiniGrid-GoToObject-6x6-N2-v0` - `MiniGrid-GoToObject-8x8-N2-v0` """ def __init__(self, size=6, numObjs=2, max_steps: int | None = None, **kwargs): self.numObjs = numObjs self.size = size # Types of objects to be generated self.obj_types = ["key", "ball", "box"] mission_space = MissionSpace( mission_func=self._gen_mission, ordered_placeholders=[COLOR_NAMES, self.obj_types], ) if max_steps is None: max_steps = 5 * 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, obj_type: str): return f"go to the {color} {obj_type}" def _gen_grid(self, width, height): self.grid = Grid(width, height) # Generate the surrounding walls self.grid.wall_rect(0, 0, width, height) # Types and colors of objects we can generate types = ["key", "ball", "box"] objs = [] objPos = [] # Until we have generated all the objects while len(objs) < self.numObjs: objType = self._rand_elem(types) objColor = self._rand_elem(COLOR_NAMES) # If this object already exists, try again if (objType, objColor) in objs: continue if objType == "key": obj = Key(objColor) elif objType == "ball": obj = Ball(objColor) elif objType == "box": obj = Box(objColor) else: raise ValueError( "{} object type given. Object type can only be of values key, ball and box.".format( objType ) ) pos = self.place_obj(obj) objs.append((objType, objColor)) objPos.append(pos) # Randomize the agent start position and orientation self.place_agent() # Choose a random object to be picked up objIdx = self._rand_int(0, len(objs)) self.targetType, self.target_color = objs[objIdx] self.target_pos = objPos[objIdx] descStr = f"{self.target_color} {self.targetType}" self.mission = "go to the %s" % descStr # print(self.mission) def step(self, action): obs, reward, terminated, truncated, info = super().step(action) ax, ay = self.agent_pos tx, ty = self.target_pos # Toggle/pickup action terminates the episode if action == self.actions.toggle: terminated = True # Reward performing the done action next to the target object 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