timandClaude Fable 5
8f2449a408
BridzikNet (trup + guess/play/value hlavy s maskovanim), self-play generator so zdielanou sietou na 4 sedadlach a opponent mixingom (random/heuristicke sedadla pre robustnost), vlastny clipped-PPO so skalovanim odmien a lr/entropy annealom. Torch je len trenovacia zavislost na hoste (requirements-rl.txt); checkpointy a logy su gitignorovane. Spustenie: py -m rl.train. Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
43 lines
1.6 KiB
Python
43 lines
1.6 KiB
Python
"""Natrenovana siet ako hrac so standardnym rozhranim guess/play.
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Rovnake rozhranie ako rl/players.py, takze funguje v rl/evaluate.py aj ako
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boti "mozog" v api/bots.py. Hrac vidi len observaciu + masku z rl/encoding.py
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-- z principu nemoze podvadzat (do cudzich ruk sa nedostane).
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"""
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import torch
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from rl.encoding import encode_observation, guess_mask, play_mask
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from rl.model import BridzikNet, mask_tensor, masked_categorical, obs_tensor
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class NeuralPlayer:
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def __init__(self, net: BridzikNet, greedy: bool = True):
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self.net = net
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self.greedy = greedy # argmax pri evaluacii; sampling pre pestrost
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def _act(self, rnd, seat: int, use_play_head: bool) -> int:
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obs = obs_tensor(encode_observation(rnd, seat)).unsqueeze(0)
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mask = mask_tensor(
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play_mask(rnd, seat) if use_play_head else guess_mask(rnd)
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).unsqueeze(0)
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self.net.eval()
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with torch.no_grad():
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guess_logits, play_logits, _ = self.net(obs)
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logits = play_logits if use_play_head else guess_logits
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logits = logits.masked_fill(~mask, float('-inf'))
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if self.greedy:
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return int(logits.argmax(dim=-1).item())
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return int(masked_categorical(logits, mask).sample().item())
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def guess(self, rnd, seat: int) -> int:
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return self._act(rnd, seat, use_play_head=False)
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def play(self, rnd, seat: int) -> int:
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return self._act(rnd, seat, use_play_head=True)
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def load_player(checkpoint_path: str, greedy: bool = True) -> NeuralPlayer:
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from rl.train import load_checkpoint
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return NeuralPlayer(load_checkpoint(checkpoint_path), greedy=greedy)
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