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Research questionHow can non-incremental tree learners handle distribution shift in online self-play reinforcement learning?As self-play policies improve, the distribution of observed game states changes continuously. Non-incremental tree learners typically require batch refitting, making it difficult to adapt to this shifting data during reinforcement learning.
AI
Machine Learning
Multi-agent Systems
Reinforcement Learning
Latest papersRecent research connected to this question, newest first.Local Updates, Global Learning (LUGL): Playing Games with non-incremental LearnersThe evidence concerns LightGBM-based agents trained from tabular updates across four perfect-information and five imperfect-information games, with comparisons to DQN and DeepCFR. The findings are limited to these game benchmarks and configurations.research paper · Sep 3, 2026
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