325 lines
8.7 KiB
Python
325 lines
8.7 KiB
Python
"""Maze tasks that are defined by their map, termination condition, and goals.
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"""
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from abc import ABC, abstractmethod
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from typing import Dict, List, NamedTuple, Tuple, Type
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import numpy as np
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from mujoco_maze.maze_env_utils import MazeCell
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class Rgb(NamedTuple):
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red: float
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green: float
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blue: float
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RED = Rgb(0.7, 0.1, 0.1)
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GREEN = Rgb(0.1, 0.7, 0.1)
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BLUE = Rgb(0.1, 0.1, 0.7)
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class MazeGoal:
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THRESHOLD: float = 0.6
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def __init__(
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self, pos: np.ndarray, reward_scale: float = 1.0, rgb: Rgb = RED
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) -> None:
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assert 0.0 <= reward_scale <= 1.0
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self.pos = pos
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self.dim = pos.shape[0]
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self.reward_scale = reward_scale
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self.rgb = rgb
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def rbga_str(self) -> str:
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r, g, b = self.rgb
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return f"{r} {g} {b} 1"
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def neighbor(self, obs: np.ndarray) -> float:
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return np.linalg.norm(obs[: self.dim] - self.pos) <= self.THRESHOLD
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def euc_dist(self, obs: np.ndarray) -> float:
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return np.sum(np.square(obs[: self.dim] - self.pos)) ** 0.5
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class Scaling(NamedTuple):
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ant: float
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point: float
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swimmer: float
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class MazeTask(ABC):
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REWARD_THRESHOLD: float
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MAZE_SIZE_SCALING: Scaling = Scaling(8.0, 4.0, 4.0)
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INNER_REWARD_SCALING: float = 0.01
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TOP_DOWN_VIEW: bool = False
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OBSERVE_BLOCKS: bool = False
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PUT_SPIN_NEAR_AGENT: bool = False
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def __init__(self, scale: float) -> None:
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self.goals = []
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self.scale = scale
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def sample_goals(self) -> bool:
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return False
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def termination(self, obs: np.ndarray) -> bool:
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for goal in self.goals:
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if goal.neighbor(obs):
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return True
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return False
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@abstractmethod
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def reward(self, obs: np.ndarray) -> float:
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pass
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@staticmethod
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@abstractmethod
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def create_maze() -> List[List[MazeCell]]:
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pass
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class DistRewardMixIn:
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REWARD_THRESHOLD: float = -1000.0
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goals: List[MazeGoal]
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scale: float
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def reward(self, obs: np.ndarray) -> float:
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return -self.goals[0].euc_dist(obs) / self.scale
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class GoalRewardSimpleRoom(MazeTask):
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""" Very easy task. For testing.
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"""
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REWARD_THRESHOLD: float = 0.9
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def __init__(self, scale: float) -> None:
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super().__init__(scale)
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self.goals = [MazeGoal(np.array([2.0 * scale, 0.0]))]
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def reward(self, obs: np.ndarray) -> float:
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return 1.0 if self.termination(obs) else -0.0001
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@staticmethod
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def create_maze() -> List[List[MazeCell]]:
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E, B, R = MazeCell.EMPTY, MazeCell.BLOCK, MazeCell.ROBOT
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return [
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[B, B, B, B, B],
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[B, R, E, E, B],
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[B, B, B, B, B],
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]
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class DistRewardSimpleRoom(GoalRewardSimpleRoom, DistRewardMixIn):
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pass
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class GoalRewardUMaze(MazeTask):
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REWARD_THRESHOLD: float = 0.9
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def __init__(self, scale: float) -> None:
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super().__init__(scale)
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self.goals = [MazeGoal(np.array([0.0, 2.0 * scale]))]
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def reward(self, obs: np.ndarray) -> float:
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return 1.0 if self.termination(obs) else -0.0001
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@staticmethod
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def create_maze() -> List[List[MazeCell]]:
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E, B, R = MazeCell.EMPTY, MazeCell.BLOCK, MazeCell.ROBOT
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return [
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[B, B, B, B, B],
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[B, R, E, E, B],
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[B, B, B, E, B],
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[B, E, E, E, B],
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[B, B, B, B, B],
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]
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class DistRewardUMaze(GoalRewardUMaze, DistRewardMixIn):
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pass
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class GoalRewardPush(GoalRewardUMaze):
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TOP_DOWN_VIEW = True
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def __init__(self, scale: float) -> None:
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super().__init__(scale)
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self.goals = [MazeGoal(np.array([0.0, 2.375 * scale]))]
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@staticmethod
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def create_maze() -> List[List[MazeCell]]:
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E, B, R = MazeCell.EMPTY, MazeCell.BLOCK, MazeCell.ROBOT
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return [
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[B, B, B, B, B],
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[B, E, R, B, B],
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[B, E, MazeCell.XY, E, B],
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[B, B, E, B, B],
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[B, B, B, B, B],
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]
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class DistRewardPush(GoalRewardPush, DistRewardMixIn):
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pass
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class GoalRewardFall(GoalRewardUMaze):
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TOP_DOWN_VIEW = True
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def __init__(self, scale: float) -> None:
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super().__init__(scale)
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self.goals = [MazeGoal(np.array([0.0, 3.375 * scale, 4.5]))]
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@staticmethod
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def create_maze() -> List[List[MazeCell]]:
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E, B, C, R = MazeCell.EMPTY, MazeCell.BLOCK, MazeCell.CHASM, MazeCell.ROBOT
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return [
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[B, B, B, B],
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[B, R, E, B],
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[B, E, MazeCell.YZ, B],
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[B, C, C, B],
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[B, E, E, B],
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[B, B, B, B],
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]
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class DistRewardFall(GoalRewardFall, DistRewardMixIn):
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pass
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class GoalReward2Rooms(MazeTask):
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REWARD_THRESHOLD: float = 0.9
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MAZE_SIZE_SCALING: Scaling = Scaling(4.0, 4.0, 4.0)
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def __init__(self, scale: float) -> None:
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super().__init__(scale)
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self.goals = [MazeGoal(np.array([0.0, 4.0 * scale]))]
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def reward(self, obs: np.ndarray) -> float:
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for goal in self.goals:
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if goal.neighbor(obs):
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return goal.reward_scale
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return -0.0001
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@staticmethod
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def create_maze() -> List[List[MazeCell]]:
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E, B, R = MazeCell.EMPTY, MazeCell.BLOCK, MazeCell.ROBOT
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return [
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[B, B, B, B, B, B, B, B],
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[B, R, E, E, E, E, E, B],
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[B, E, E, E, E, E, E, B],
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[B, B, B, B, B, E, B, B],
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[B, E, E, E, E, E, E, B],
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[B, E, E, E, E, E, E, B],
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[B, B, B, B, B, B, B, B],
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]
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class DistReward2Rooms(GoalReward2Rooms, DistRewardMixIn):
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pass
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class SubGoal2Rooms(GoalReward2Rooms):
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def __init__(self, scale: float) -> None:
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super().__init__(scale)
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self.goals.append(MazeGoal(np.array([5.0 * scale, 0.0 * scale]), 0.5, GREEN))
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class GoalReward4Rooms(MazeTask):
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REWARD_THRESHOLD: float = 0.9
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MAZE_SIZE_SCALING: Scaling = Scaling(4.0, 4.0, 4.0)
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def __init__(self, scale: float) -> None:
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super().__init__(scale)
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self.goals = [MazeGoal(np.array([6.0 * scale, -6.0 * scale]))]
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def reward(self, obs: np.ndarray) -> float:
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for goal in self.goals:
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if goal.neighbor(obs):
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return goal.reward_scale
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return -0.0001
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@staticmethod
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def create_maze() -> List[List[MazeCell]]:
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E, B, R = MazeCell.EMPTY, MazeCell.BLOCK, MazeCell.ROBOT
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return [
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[B, B, B, B, B, B, B, B, B],
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[B, E, E, E, B, E, E, E, B],
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[B, E, E, E, E, E, E, E, B],
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[B, E, E, E, B, E, E, E, B],
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[B, B, E, B, B, B, E, B, B],
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[B, E, E, E, B, E, E, E, B],
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[B, E, E, E, E, E, E, E, B],
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[B, R, E, E, B, E, E, E, B],
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[B, B, B, B, B, B, B, B, B],
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]
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class DistReward4Rooms(GoalReward4Rooms, DistRewardMixIn):
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pass
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class SubGoal4Rooms(GoalReward4Rooms):
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def __init__(self, scale: float) -> None:
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super().__init__(scale)
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self.goals += [
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MazeGoal(np.array([0.0 * scale, -6.0 * scale]), 0.5, GREEN),
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MazeGoal(np.array([6.0 * scale, 0.0 * scale]), 0.5, GREEN),
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]
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class GoalRewardTRoom(MazeTask):
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REWARD_THRESHOLD: float = 0.9
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MAZE_SIZE_SCALING: Scaling = Scaling(4.0, 4.0, 4.0)
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def __init__(
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self, scale: float, goals: List[Tuple[float, float]] = [(2.0, -3.0)],
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) -> None:
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super().__init__(scale)
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self.goals = []
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for x, y in goals:
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self.goals.append(MazeGoal(np.array([x * scale, y * scale])))
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def reward(self, obs: np.ndarray) -> float:
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for goal in self.goals:
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if goal.neighbor(obs):
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return goal.reward_scale
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return -0.0001
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@staticmethod
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def create_maze() -> List[List[MazeCell]]:
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E, B, R = MazeCell.EMPTY, MazeCell.BLOCK, MazeCell.ROBOT
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return [
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[B, B, B, B, B, B, B],
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[B, E, E, B, E, E, B],
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[B, E, E, B, E, E, B],
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[B, E, B, B, B, E, B],
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[B, E, E, R, E, E, B],
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[B, B, B, B, B, B, B],
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]
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class DistRewardTRoom(GoalRewardTRoom, DistRewardMixIn):
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pass
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class TaskRegistry:
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REGISTRY: Dict[str, List[Type[MazeTask]]] = {
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"SimpleRoom": [DistRewardSimpleRoom, GoalRewardSimpleRoom],
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"UMaze": [DistRewardUMaze, GoalRewardUMaze],
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"Push": [DistRewardPush, GoalRewardPush],
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"Fall": [DistRewardFall, GoalRewardFall],
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"2Rooms": [DistReward2Rooms, GoalReward2Rooms, SubGoal2Rooms],
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"4Rooms": [DistReward4Rooms, GoalReward4Rooms, SubGoal4Rooms],
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"TRoom": [DistRewardTRoom, GoalRewardTRoom],
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}
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@staticmethod
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def keys() -> List[str]:
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return list(TaskRegistry.REGISTRY.keys())
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@staticmethod
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def tasks(key: str) -> List[Type[MazeTask]]:
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return TaskRegistry.REGISTRY[key]
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