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7248ace | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 | import numpy as np
ACTIONS = {
1: (0, -1),
2: (-1, 0),
3: (0, 1),
4: (1, 0),
}
class AmidarEnvironment:
"""Trace a rectilinear maze while avoiding deterministic patrol enemies."""
score_cap = 9.0
def __init__(self, sticky_action_prob: float = 0.1, horizon: int = 600):
self.sticky_action_prob = sticky_action_prob
self.horizon = horizon
self.random = np.random.RandomState()
self.seed(0)
self.reset()
@classmethod
def normalize_score(cls, raw_score: float) -> float:
return min(max(raw_score, 0.0), cls.score_cap) / cls.score_cap
def seed(self, seed: int) -> None:
self.random.seed(seed)
def reset(self) -> None:
self.player = [9, 0]
self.previous_player = self.player.copy()
self.enemies = [[0, 0], [0, 9], [9, 9]]
self.previous_enemies = [position.copy() for position in self.enemies]
self.enemy_directions = [4, 1, 2]
self.painted_edges: set[tuple[tuple[int, int], tuple[int, int]]] = set()
self.completed_boxes: set[tuple[int, int]] = set()
self.shadow_timer = 0
self.shadow_uses = 3
self.last_action = 0
self.steps = 0
self.terminal = False
@staticmethod
def _on_path(position: list[int] | tuple[int, int]) -> bool:
row, column = position
return 0 <= row < 10 and 0 <= column < 10 and (row % 3 == 0 or column % 3 == 0)
@staticmethod
def _edge(first: list[int], second: list[int]) -> tuple[tuple[int, int], tuple[int, int]]:
return tuple(sorted((tuple(first), tuple(second))))
def act(self, action: int) -> tuple[float, bool]:
if self.terminal:
return 0.0, True
if self.random.rand() < self.sticky_action_prob:
action = self.last_action
self.last_action = action
self.previous_player = self.player.copy()
if action == 5 and self.shadow_uses > 0 and self.shadow_timer == 0:
self.shadow_uses -= 1
self.shadow_timer = 12
elif action in ACTIONS:
row_delta, column_delta = ACTIONS[action]
target = [self.player[0] + row_delta, self.player[1] + column_delta]
if self._on_path(target):
self.player = target
if self.player != self.previous_player:
self.painted_edges.add(self._edge(self.previous_player, self.player))
reward = self._complete_boxes()
self.previous_enemies = [position.copy() for position in self.enemies]
if self.shadow_timer == 0:
for index in range(len(self.enemies)):
self._move_enemy(index)
if self.shadow_timer == 0 and any(enemy == self.player for enemy in self.enemies):
self.terminal = True
self.shadow_timer = max(0, self.shadow_timer - 1)
self.steps += 1
if len(self.completed_boxes) == 9 or self.steps >= self.horizon:
self.terminal = True
return reward, self.terminal
def _move_enemy(self, index: int) -> None:
direction = self.enemy_directions[index]
preferences = (direction, {1: 2, 2: 3, 3: 4, 4: 1}[direction], {1: 4, 2: 1, 3: 2, 4: 3}[direction])
for candidate_direction in preferences:
row_delta, column_delta = ACTIONS[candidate_direction]
target = [self.enemies[index][0] + row_delta, self.enemies[index][1] + column_delta]
if self._on_path(target):
self.enemies[index] = target
self.enemy_directions[index] = candidate_direction
return
def _complete_boxes(self) -> float:
reward = 0.0
for box_row in range(3):
for box_column in range(3):
box = (box_row, box_column)
if box in self.completed_boxes:
continue
top = box_row * 3
left = box_column * 3
required = set()
for offset in range(3):
required.add(self._edge([top, left + offset], [top, left + offset + 1]))
required.add(self._edge([top + 3, left + offset], [top + 3, left + offset + 1]))
required.add(self._edge([top + offset, left], [top + offset + 1, left]))
required.add(self._edge([top + offset, left + 3], [top + offset + 1, left + 3]))
if required <= self.painted_edges:
self.completed_boxes.add(box)
reward += 1.0
return reward
def state(self) -> np.ndarray:
state = np.zeros((10, 10, 6), dtype=bool)
state[*self.player, 0] = True
for enemy in self.enemies:
channel = 3 if self.shadow_timer > 0 else 1
state[*enemy, channel] = True
for box_row in range(3):
for box_column in range(3):
center = (min(9, box_row * 3 + 1), min(9, box_column * 3 + 1))
if (box_row, box_column) not in self.completed_boxes:
state[*center, 2] = True
for first, second in self.painted_edges:
state[*first, 4] = True
state[*second, 4] = True
state[9, : self.shadow_uses, 5] = True
if self.shadow_timer > 0:
state[8, : min(10, self.shadow_timer), 5] = True
return state
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