sankalphs/duel-tiny-fighter

Duel Tiny Fighter (78,863 parameters)

A real-time CPU policy network for NPC move selection in a 3D fighting game. Runs in <1ms per inference on CPU, conditioned on Nemotron strategic weights.

Architecture

Layer	Shape	Notes
Linear	168 → 256	One-hot move history + scalars
LayerNorm	256	Stable at batch=1 inference
ReLU + Dropout(0.1)
Linear	256 → 128
LayerNorm	128
ReLU + Dropout(0.1)
Linear	128 → 15	Logits over 15 moves

Total parameters: 78,863

Move Vocabulary

jab, cross, hook, kick, uppercut, block, parry, dodge, advance, retreat, grapple, throw, sweep, feint, wait

Input Features (168-dim)

• Last 5 NPC moves (5 × 15 one-hot = 75)
• Last 5 player moves (5 × 15 one-hot = 75)
• HP difference, stamina difference (2)
• Distance one-hot (3)
• Strategy weights: aggression, defense, parry_affinity, kick_affinity, grapple_affinity (5)
• Round normalised (1)
• Absolute HP, stamina for both (4)
• Padding to 168

Inference

import torch
from tiny_fighter import TinyFighter, state_to_features, make_move_mask

model = TinyFighter()
model.load_state_dict(torch.load("tiny_fighter.pt", map_location="cpu"), strict=False)
model.eval()

feats = state_to_features(
    last_npc_moves=["jab", "block"],
    last_player_moves=["cross", "retreat"],
    player_hp=80.0, npc_hp=50.0,
    player_stamina=60.0, npc_stamina=40.0,
    distance="mid",
    aggression=0.7, defense=0.3,
    parry_affinity=0.4, kick_affinity=0.6,
    grapple_affinity=0.2,
)
mask = make_move_mask("mid")

with torch.inference_mode():
    logits = model.predict(feats, mask)
    move = logits.softmax(-1).argmax().item()

print(f"Selected: {model.MOVES[move]}")

Training

Trained on 20k procedurally generated (state, strategy_weights) → move examples using supervised learning on CPU. The model learns to map Nemotron's strategic direction (aggressive/defensive/grappling) into concrete move probabilities.

Part of Duel of Nemotron

• Strategist: Nemotron 3 Nano 4B (fine-tuned, Modal A10)
• Executor: This tiny model (CPU, <1ms)
• Game: React + Three.js 3D fighting game

Built for the Build Small Hackathon by @sankalphs.