Model: DQN policy that powers the Neural difficulty tier in Lil Maina Combat, a 2D fighting game (Java 26 / Gradle). It learns to fight by self-play against a scripted rule-based opponent and beats every built-in difficulty tier.
Q-values over 6 actions (attack, block, approach, retreat, jump, hold)
File
fighter_policy.bin (~42 KB, custom binary LFCDQN1, version 1, double precision)
License
MIT
Model type
DQN (Deep Q-Network), greedy argmax policy
State features (normalized)
My X / arena width
My Y / ground level
My health / max health
Opponent X / arena width
Opponent Y / ground level
Opponent health / max health
Horizontal distance / arena width
Vertical offset / ground level
My facing direction
Opponent facing direction
Features are mirrored per-agent so the same policy plays both sides of the fight.
Training
Algorithm: DQN with target network and experience replay (replay buffer capacity 100k).
Opponent: rule-based controller at the Hard tier, played in a 600-tick time-capped match.
Reward: health-lead shaping each tick, knockout bonus, anti-turtle penalty so holding at range is never free.
Exploration: epsilon annealed from 1.0 to 0.05 across a 30k-step final drain; best checkpoint kept by greedy ladder evaluation during training.
Evaluation
Greedy (argmax) policy vs. each built-in rule tier, on two independent 200-game ladder evals (1200 games):
Opponent
Win rate
Easy
100%
Medium
100%
Hard
100%
0 losses across all 1200 games. The GUI Neural tier plays this exact greedy policy.
Usage
NeuralNet net = ModelPersistence.loadOrNull("fighter_policy.bin");
The game ships the file as fighter_policy.bin and embeds it in packaged builds (jar / AppImage / Windows .exe) so the Neural tier works self-contained.
Limitations
Single trained policy; tuned against the rule-based opponent family, not against human playstyles or other learned agents.
Greedy action selection only (no exploration at inference).
Q-values are not calibrated probabilities; treat as relative preferences.
