43 citations · 99 across the 31 of their papers we have counts for
3 papers · 1 filter
Local Updates, Global Learning (LUGL): Playing Games with non-incremental Learners
David Milec, Spyridon Samothrakis, Michael Fairbank +1
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 trainin…
For How Long Should We Be Punching? Learning Action Duration in Fighting Games
Hoang Hai Nguyen, Kurt Driessens, Dennis J. N. J. Soemers
Fighting games such as Street Fighter II present unique challenges to reinforcement learning (RL) agents due to their fast-paced, real-time nature. In most RL frameworks, agents ar…
StratFormer: Adaptive Opponent Modeling and Exploitation in Imperfect-Information Games
Andy Caen, Mark H. M. Winands, Dennis J. N. J. Soemers
We present StratFormer, a transformer-based meta-agent that learns to simultaneously model and exploit opponents in imperfect-information games through a two-phase curriculum. The…