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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 | import numpy as np
HORIZON = 320
SCHOOL_INTERVAL = 8
MAX_FISH = (HORIZON // SCHOOL_INTERVAL) * 3
ANCHOR = (3, 0)
class CastNEnvironment:
score_cap = MAX_FISH * (MAX_FISH + 1) // 2
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.mode = 0
self.power = 0
self.water = 50
self.steps = 0
self.last_action = 0
self.lure = ANCHOR
self.fish: set[tuple[int, int, bool]] = set()
self.caught: list[bool] = []
self.terminal = False
@classmethod
def normalize_score(cls, score: float) -> float:
return min(max(score, 0), cls.score_cap) / cls.score_cap
def _line(self) -> set[tuple[int, int]]:
row, column = self.lure
if column == 0:
return {ANCHOR}
return {(round(ANCHOR[0] + (row - ANCHOR[0]) * x / column), x)
for x in range(column + 1)}
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
pressed = action == 5 and self.last_action != 5
self.last_action = action
self.steps += 1
if self.mode == 0 and pressed:
self.mode, self.power = 1, 1
elif self.mode == 1:
if action == 5 and self.power < 3:
self.power += 1
else:
self.lure = (2, self.power * 3)
self.mode = 2
elif self.mode == 2 and pressed:
self.mode = 3
if self.mode == 2:
row, column = self.lure
self.lure = (min(9, row + 1), column)
if self.steps % 2 == 0:
surface = min(8, self.water // 10 + 1)
self.fish = {(max(surface, row), column - 1, red)
for row, column, red in self.fish if column > 0}
if self.steps % SCHOOL_INTERVAL == 0:
red = self.steps % (SCHOOL_INTERVAL * 4) == 0
depth = min(7, self.water // 10 + 1 + int(red))
row = int(self.random.integers(depth, 8))
self.fish.update((row + offset, 9, red) for offset in range(3))
reward = 0
if self.mode == 3:
line = self._line()
hooked = {fish for fish in self.fish if fish[:2] in line}
self.fish.difference_update(hooked)
self.caught.extend(red for _, _, red in sorted(hooked))
row, column = self.lure
if column <= 1:
for index, red in enumerate(self.caught, 1):
reward += -1 if red else index
self.water = max(0, min(50, self.water + (-5 if red else 1)))
self.caught.clear()
self.lure, self.mode = ANCHOR, 0
else:
self.lure = (row + int(np.sign(ANCHOR[0] - row)), column - 1)
if self.steps % 2 == 0:
self.water = max(0, self.water - 1)
self.terminal = self.steps >= self.horizon or (self.mode != 3 and self.water < 26)
return float(reward), self.terminal
def state(self) -> np.ndarray:
state = np.zeros((10, 10, 6), dtype=bool)
state[self.lure[0], self.lure[1], 0] = True
for row, column, red in self.fish:
state[row, column, 1 if red else 2] = True
for row, column in self._line():
state[row, column, 3] = True
state[self.water // 10, :, 4] = True
values = (self.mode, self.power, self.water, self.steps,
len(self.caught), int(self.last_action == 5))
for row, value in enumerate(values):
for bit in range(9):
state[row, bit, 5] = bool(value & (1 << bit))
for index, red in enumerate(self.caught):
state[6 + index // 10, index % 10, 5] = red
return state
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