Datasets:
File size: 2,693 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 | from dataclasses import dataclass
import numpy as np
HORIZON = 400
FRAMES_PER_STEP = 4
@dataclass
class Spike:
x: float
y: float
class BottopEnvironment:
score_cap = HORIZON * FRAMES_PER_STEP / 10
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 = 0
self.spawn_distance = self.height = self.velocity = 0.0
self.jumping = self.pressed = self.terminal = False
self.spikes: list[Spike] = []
@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:
self.pressed = action == 5
self.steps += 1
frames = 0
for _ in range(FRAMES_PER_STEP):
self._advance_frame()
frames += 1
if self.terminal:
break
self.terminal |= self.steps >= self.horizon
return frames / 10, self.terminal
def _advance_frame(self) -> None:
self.spawn_distance -= 1
if self.spawn_distance < 0:
if self.random.random() < 0.33:
y = float(self.random.choice((8, 92)))
else:
y = self.random.uniform(8, 92)
self.spikes.append(Spike(103, y))
self.spawn_distance += self.random.uniform(30, 60)
for spike in self.spikes:
spike.x -= 1
self.spikes = [spike for spike in self.spikes if spike.x > -3]
if not self.jumping and self.pressed:
self.jumping = True
self.velocity = 3
if self.jumping:
self.velocity -= 0.1 if self.pressed else 0.3
self.height += self.velocity
if self.height < 0:
self.height = self.velocity = 0
self.jumping = False
top, bottom = 8 + self.height, 92 - self.height
self.terminal = any(
abs(spike.x - 9) < 6 and min(abs(spike.y - top), abs(spike.y - bottom)) < 6
for spike in self.spikes
)
def state(self) -> np.ndarray:
state = np.zeros((10, 10, 6), dtype=bool)
state[int(8 + self.height) // 10, 0, 0] = True
state[int(92 - self.height) // 10, 0, 1] = True
for spike in self.spikes:
if 0 <= spike.x < 100:
state[int(spike.y) // 10, int(spike.x) // 10, 2] = True
state[(0, 9), :, 3] = True
state[0, :3, 4] = self.pressed, self.jumping, self.velocity > 0
for bit in range(9):
state[9, bit, 5] = bool(self.steps & (1 << bit))
return state
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