Local Updates, Global Learning (LUGL): Playing Games with non-incremental Learners
The dominance of Neural Networks (NNs) in RL is partially due to their incremental learning capability, which naturally suits the online, non-stationary nature of self-play training. However, gradient-boosted trees like LightGBM are widely recognised as the state of the art for tabular data in supervised learning, often outperforming NNs in accuracy and efficiency. Game states are inherently tabular---discrete actions, categorical card identities, structured board positions---which makes them an ideal candidate for tree-based methods. We introduce LUGL (Local Updates, Global Learning), a framework that decouples data collection from model fitting, enabling non-incremental learners such as...
| Authors | David Milec, Spyridon Samothrakis, Michael Fairbank et al. |
|---|---|
| Primary category | cs.LG |
| Published | 3 September 2026 |
| Last updated at source | 3 September 2026 |
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