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026f63e | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 | """Scripted Snake agents: the oracle used for supervision and the baselines.
All agents implement `act(state: SnakeState) -> str`.
Path search is *time-aware*: a body cell at index i (head=0) of a snake of
length L becomes free after L - i moves, so a path may pass where the tail
currently is if it arrives late enough. This makes BFS paths exact as long as
no food is eaten along the way (food is only ever the final cell).
"""
from __future__ import annotations
from collections import deque
from functools import lru_cache
import numpy as np
from .env import ACTIONS, DELTAS, SnakeState
@lru_cache(maxsize=16)
def _neighbors(h: int, w: int) -> tuple[tuple[tuple[int, str], ...], ...]:
out = []
for cell in range(h * w):
r, c = divmod(cell, w)
nb = []
for a in ACTIONS:
dr, dc = DELTAS[a]
nr, nc = r + dr, c + dc
if 0 <= nr < h and 0 <= nc < w:
nb.append((nr * w + nc, a))
out.append(tuple(nb))
return tuple(out)
def _flat(state: SnakeState) -> tuple[list[int], int]:
w = state.width
body = [r * w + c for r, c in state.body]
food = state.food[0] * w + state.food[1] if state.food is not None else -1
return body, food
def _free_times(body: list[int], area: int) -> list[int]:
ft = [0] * area
L = len(body)
for i, cell in enumerate(body):
ft[cell] = L - i
return ft
def _bfs(nbrs, area: int, start: int, ft: list[int], goal: int = -1):
dist = [-1] * area
parent = [-1] * area
dist[start] = 0
q = deque([start])
while q:
u = q.popleft()
du = dist[u] + 1
for v, _ in nbrs[u]:
if dist[v] < 0 and du >= ft[v]:
dist[v] = du
parent[v] = u
if v == goal:
return dist, parent
q.append(v)
return dist, parent
def _reachable_count(nbrs, area: int, start: int, ft: list[int]) -> int:
dist, _ = _bfs(nbrs, area, start, ft)
return sum(1 for d in dist if d > 0)
def _tail_reachable(nbrs, area: int, body: list[int]) -> tuple[bool, int]:
if len(body) < 2:
return True, 0
ft = _free_times(body, area)
tail = body[-1]
dist, _ = _bfs(nbrs, area, body[0], ft, goal=tail)
return dist[tail] > 0, dist[tail]
def safe_moves(state: SnakeState) -> list[str]:
"""Moves that do not die on the very next step."""
body, _ = _flat(state)
nbrs = _neighbors(state.height, state.width)
ft = _free_times(body, state.height * state.width)
return [a for v, a in nbrs[body[0]] if ft[v] <= 1]
class Agent:
name = "agent"
def act(self, state: SnakeState) -> str: # pragma: no cover - interface
raise NotImplementedError
def reset(self, seed: int | None = None) -> None:
pass
class RandomAgent(Agent):
name = "random"
def __init__(self, seed: int = 0):
self.rng = np.random.default_rng(seed)
def reset(self, seed=None):
if seed is not None:
self.rng = np.random.default_rng(seed)
def act(self, state):
return ACTIONS[int(self.rng.integers(4))]
class GreedyFoodAgent(Agent):
"""Minimise Manhattan distance to food; avoid *immediately* fatal moves when possible."""
name = "greedy"
def act(self, state):
safe = safe_moves(state) or list(ACTIONS)
hr, hc = state.body[0]
fr, fc = state.food if state.food else (hr, hc)
return min(safe, key=lambda a: abs(hr + DELTAS[a][0] - fr) + abs(hc + DELTAS[a][1] - fc))
class BFSAgent(Agent):
"""Shortest (time-aware) path to food, no look-ahead; else the safe move with most space."""
name = "bfs"
def act(self, state):
h, w = state.height, state.width
area = h * w
nbrs = _neighbors(h, w)
body, food = _flat(state)
ft = _free_times(body, area)
if food >= 0:
path = _path_to(nbrs, area, body[0], ft, food)
if path:
return _direction(w, body[0], path[0])
return _max_space_move(nbrs, area, body) or ACTIONS[0]
class SurvivalAgent(Agent):
"""Flood-fill heuristic: maximise reachable space, break ties by distance to food."""
name = "survival"
def act(self, state):
h, w = state.height, state.width
area = h * w
nbrs = _neighbors(h, w)
body, food = _flat(state)
ft = _free_times(body, area)
best, best_key = None, None
fr, fc = divmod(food, w) if food >= 0 else divmod(body[0], w)
for v, a in nbrs[body[0]]:
if ft[v] > 1:
continue
nb = [v] + (body if v == food else body[:-1])
space = _reachable_count(nbrs, area, v, _free_times(nb, area))
r, c = divmod(v, w)
key = (min(space, len(nb) + 1), -(abs(r - fr) + abs(c - fc)))
if best_key is None or key > best_key:
best, best_key = a, key
return best or ACTIONS[0]
class OracleAgent(Agent):
"""Strong teacher: eat only if the snake can still reach its tail afterwards,
otherwise chase the tail along the longest safe route, otherwise maximise space.
`analyze` also returns the *set* of equally good moves. Ties are broken by a fixed
priority (UP > DOWN > LEFT > RIGHT) instead of BFS queue order, so the hard label
is a learnable function of the state (and this rule also scored best: 95.0 vs
91.5-92.1 for "close the larger/smaller axis gap first", 60 games 10x10).
"""
name = "oracle"
def act(self, state):
return self.analyze(state)[0]
def analyze(self, state) -> tuple[str, list[str], str]:
"""-> (move, optimal_moves, mode) with mode in {eat, tail, space, dead}."""
h, w = state.height, state.width
area = h * w
nbrs = _neighbors(h, w)
body, food = _flat(state)
L = len(body)
ft = _free_times(body, area)
head = body[0]
safe = [(v, a) for v, a in nbrs[head] if ft[v] <= 1]
if not safe:
return ACTIONS[0], list(ACTIONS), "dead"
if food >= 0:
dist, _ = _bfs(nbrs, area, head, ft, goal=food)
d = dist[food]
if d > 0:
good = []
for v, a in safe:
if v == food:
virtual = [v] + body
else:
nb = [v] + body[:-1]
path = _path_to(nbrs, area, v, _free_times(nb, area), food)
if len(path) != d - 1:
continue
virtual = (path[::-1] + nb)[: L + 1]
if len(virtual) >= area or _tail_reachable(nbrs, area, virtual)[0]:
good.append(a)
if good:
return good[0], good, "eat"
# tail chasing: keep an escape route, prefer the move farthest from the tail
cands = []
for v, a in safe:
nb = [v] + (body if v == food else body[:-1])
ok, dt = _tail_reachable(nbrs, area, nb)
if ok:
cands.append((dt, a))
if cands:
best = max(dt for dt, _ in cands)
good = [a for dt, a in cands if dt == best]
return good[0], good, "tail"
spaces = []
for v, a in safe:
nb = [v] + (body if v == food else body[:-1])
spaces.append((_reachable_count(nbrs, area, v, _free_times(nb, area)), a))
best = max(sp for sp, _ in spaces)
good = [a for sp, a in spaces if sp == best]
return good[0], good, "space"
def _path_to(nbrs, area, start, ft, goal) -> list[int]:
dist, parent = _bfs(nbrs, area, start, ft, goal)
if dist[goal] <= 0:
return []
path = [goal]
while parent[path[-1]] != start:
path.append(parent[path[-1]])
return path[::-1]
def _direction(w: int, a: int, b: int) -> str:
ar, ac = divmod(a, w)
br, bc = divmod(b, w)
return {(-1, 0): "UP", (1, 0): "DOWN", (0, -1): "LEFT", (0, 1): "RIGHT"}[(br - ar, bc - ac)]
def _max_space_move(nbrs, area, body) -> str | None:
ft = _free_times(body, area)
best, best_space = None, -1
for v, a in nbrs[body[0]]:
if ft[v] > 1:
continue
nb = [v] + body[:-1]
space = _reachable_count(nbrs, area, v, _free_times(nb, area))
if space > best_space:
best, best_space = a, space
return best
AGENTS = {c.name: c for c in (RandomAgent, GreedyFoodAgent, BFSAgent, SurvivalAgent, OracleAgent)}
def make_agent(name: str, seed: int = 0) -> Agent:
cls = AGENTS[name]
return cls(seed) if cls is RandomAgent else cls()
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