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File size: 3,215 Bytes
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 | from dataclasses import dataclass
import math
import numpy as np
HORIZON = 400
FRAMES_PER_STEP = 4
MIN_SPAWN_INTERVAL = 70
MAX_HARVEST = 100
TRACK_WIDTH = 200
@dataclass
class Stalk:
x: int
height: float = 0
speed: float = 1
class BambooEnvironment:
score_cap = math.ceil(HORIZON * FRAMES_PER_STEP / MIN_SPAWN_INTERVAL) * MAX_HARVEST
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.spawn_timer = 0
self.pressed = self.terminal = False
self.x = 190.0
self.direction = 1
self.boost = 0.0
self.stalks: list[Stalk] = []
@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
pressed = self.pressed if self.random.random() < self.sticky_action_prob else action == 5
if pressed and not self.pressed:
self.direction *= -1
self.pressed = pressed
self.steps += 1
reward = 0
for _ in range(FRAMES_PER_STEP):
reward += self._advance_frame()
if self.terminal:
break
self.terminal |= self.steps >= self.horizon
return float(reward), self.terminal
def _advance_frame(self) -> int:
self.x = (self.x + self.direction * (1 + self.boost)) % TRACK_WIDTH
self.boost *= 0.9
if self.spawn_timer == 0:
self.stalks.append(Stalk(int(self.random.integers(5, 195))))
self.spawn_timer = int(self.random.integers(MIN_SPAWN_INTERVAL, 101))
self.spawn_timer -= 1
reward = 0
survivors = []
for stalk in self.stalks:
stalk.height += 0.14 * stalk.speed
distance = abs(self.x - stalk.x)
if not self.pressed and min(distance, TRACK_WIDTH - distance) <= 6:
if 5 <= stalk.height <= 25:
grade = math.ceil((stalk.height - 5) / 3)
reward += MAX_HARVEST if grade == 7 else grade**2
continue
if stalk.height > 25:
stalk.height *= 0.7
stalk.speed *= 0.6
self.direction *= -1
self.boost = min(5, self.boost + 1)
if stalk.height <= 25:
continue
if stalk.height > 50:
stalk.speed *= 0.997
if stalk.height >= 89:
self.terminal = True
survivors.append(stalk)
self.stalks = survivors
return reward
def state(self) -> np.ndarray:
state = np.zeros((10, 10, 6), dtype=bool)
column = int(self.x) // 20
state[8, column, 0] = True
state[8, column, 1] = self.direction < 0
for stalk in self.stalks:
column = stalk.x // 20
height = max(1, math.ceil(stalk.height / 10))
state[9 - height:9, column, 2] = True
state[9 - height, column, 3] |= 5 <= stalk.height <= 25
state[9 - height, column, 4] |= 23 < stalk.height <= 25
state[9, 0, 5] = self.pressed
for bit in range(9):
state[9, bit + 1, 5] = bool(self.steps & (1 << bit))
return state
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