1 citations · 1 across the 3 of their papers we have counts for
3 papers
cs.MA2026
Teaching an Old Dynamics New Tricks: Regularization-free Last-iterate Convergence in Zero-sum Games via BNN Dynamics
Tuo Zhang, Leonardo Stella
Zero-sum games are a fundamental setting for adversarial training and decision-making in multi-agent learning (MAL). Existing methods often ensure convergence to (approximate) Nash…
cs.LG2025
Experience-replay Innovative Dynamics
Tuo Zhang, Leonardo Stella, Julian Barreiro-Gomez
Despite its groundbreaking success, multi-agent reinforcement learning (MARL) still suffers from instability and nonstationarity. Replicator dynamics, the most well-known model fro…
cs.AI2024★ 1 cited
An Evolutionary Framework for Connect-4 as Test-Bed for Comparison of Advanced Minimax, Q-Learning and MCTS
Henry Taylor, Leonardo Stella
A major challenge in decision making domains with large state spaces is to effectively select actions which maximize utility. In recent years, approaches such as reinforcement lear…