11 citations · 28 across the 8 of their papers we have counts for
3 papers · 1 filter
Self-Play PSRO: Toward Optimal Populations in Two-Player Zero-Sum Games
Stephen McAleer, JB Lanier, Kevin Wang +3
In competitive two-agent environments, deep reinforcement learning (RL) methods based on the \emph{Double Oracle (DO)} algorithm, such as \emph{Policy Space Response Oracles (PSRO)…
Feasible Adversarial Robust Reinforcement Learning for Underspecified Environments
JB Lanier, Stephen McAleer, Pierre Baldi +1
Robust reinforcement learning (RL) considers the problem of learning policies that perform well in the worst case among a set of possible environment parameter values. In real-worl…
Anytime PSRO for Two-Player Zero-Sum Games
Stephen McAleer, Kevin Wang, John Lanier +4
Policy space response oracles (PSRO) is a multi-agent reinforcement learning algorithm that has achieved state-of-the-art performance in very large two-player zero-sum games. PSRO…