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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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 | import numpy as np
SIZE = 9
CENTER = SIZE // 2
EMPTY = -1
HORIZON = 400
GROWTH_INTERVAL = 30
SETTLE_DELAY = 15
DIRECTIONS = ((0, -1), (-1, 0), (1, 0), (0, 1))
class BreedCEnvironment:
score_cap = 250
def __init__(self, sticky_action_prob: float = 0.1, horizon: int = HORIZON):
self.sticky_action_prob = sticky_action_prob
self.horizon = min(horizon, HORIZON)
self.seed(0)
self.reset()
def seed(self, seed: int) -> None:
self.random = np.random.default_rng(seed)
def reset(self) -> None:
self.steps = self.last_action = self.growth_count = self.settle = self.clears = 0
self.multiplier = 1
self.timer = GROWTH_INTERVAL
self.terminal = False
self.cursor = CENTER, CENTER
self.grid = np.full((SIZE, SIZE), EMPTY, dtype=np.int8)
self.grid[CENTER, CENTER] = self.random.integers(4)
self._grow()
@classmethod
def normalize_score(cls, score: float) -> float:
return min(max(score, 0), cls.score_cap) / cls.score_cap
def act(self, action: int) -> tuple[float, bool]:
if self.terminal:
return 0.0, True
if self.random.random() < self.sticky_action_prob:
action = self.last_action
select = action == 5 and self.last_action != 5
self.last_action = action
self.steps += 1
reward = 0
if 1 <= action <= 4:
dy, dx = DIRECTIONS[action - 1]
y, x = self.cursor
self.cursor = min(SIZE - 1, max(0, y + dy)), min(SIZE - 1, max(0, x + dx))
if self.settle:
self.settle -= 1
if not self.settle:
self._compact()
self.clears += 1
self.timer = GROWTH_INTERVAL
else:
if self.grid[CENTER, CENTER] == EMPTY:
self.grid[CENTER, CENTER] = self.random.integers(4)
if select:
reward = self._erase()
if not self.settle:
self.timer -= 1
if self.timer <= 0:
self._grow()
self.terminal = self.steps >= self.horizon or bool(np.all(self.grid != EMPTY))
return float(reward), self.terminal
def _erase(self) -> int:
color = self.grid[self.cursor]
group = set()
if color != EMPTY:
group.add(self.cursor)
pending = [self.cursor]
while pending:
y, x = pending.pop()
for dy, dx in DIRECTIONS:
ny, nx = y + dy, x + dx
neighbor = ny, nx
if 0 <= ny < SIZE and 0 <= nx < SIZE and neighbor not in group and self.grid[neighbor] == color:
group.add(neighbor)
pending.append(neighbor)
size = len(group)
if size < 4:
self._grow()
self._grow()
return -size
for cell in group:
self.grid[cell] = EMPTY
reward = size * self.multiplier
self.multiplier += 1
self.settle = SETTLE_DELAY
return reward
def _grow(self) -> None:
lines = self.grid if self.growth_count % 2 == 0 else self.grid.T
old = lines.copy()
lines[:, :CENTER - 1] = old[:, 1:CENTER]
lines[:, CENTER + 2:] = old[:, CENTER + 1:-1]
for line in lines:
color = int(line[CENTER])
if color != EMPTY:
direction = int(self.random.choice((-1, 1)))
new_color = (color + int(self.random.integers(3))) % 4
line[CENTER - direction] = color
line[CENTER] = line[CENTER + direction] = new_color
self.growth_count += 1
self.multiplier = 1
self.timer = GROWTH_INTERVAL
def _compact(self) -> None:
axes = (self.grid, self.grid.T)
if self.clears % 2:
axes = axes[::-1]
moved = True
while moved:
moved = False
for lines in axes:
for line in lines:
for start, stop, step in ((CENTER, 0, -1), (CENTER, SIZE - 1, 1)):
for index in range(start, stop, step):
source = index + step
if line[index] == EMPTY and line[source] != EMPTY:
line[index], line[source] = line[source], EMPTY
moved = True
def state(self) -> np.ndarray:
state = np.zeros((10, 10, 6), dtype=bool)
for color in range(4):
state[:SIZE, :SIZE, color] = self.grid == color
state[*self.cursor, 4] = True
for row, value in enumerate((self.steps, self.timer, self.settle, self.multiplier)):
for bit in range(9):
state[row, bit, 5] = bool(value & (1 << bit))
state[9, 0, 5] = self.last_action == 5
state[9, 1, 5] = self.growth_count % 2 == 1
return state
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