11 citations · 28 across the 8 of their papers we have counts for
8 papers
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…
Improving Social Welfare While Preserving Autonomy via a Pareto Mediator
Stephen McAleer, John Lanier, Michael Dennis +2
Machine learning algorithms often make decisions on behalf of agents with varied and sometimes conflicting interests. In domains where agents can choose to take their own action or…
XDO: A Double Oracle Algorithm for Extensive-Form Games
Stephen McAleer, John Lanier, Kevin Wang +2
Policy Space Response Oracles (PSRO) is a reinforcement learning (RL) algorithm for two-player zero-sum games that has been empirically shown to find approximate Nash equilibria in…
Pipeline PSRO: A Scalable Approach for Finding Approximate Nash Equilibria in Large Games
Stephen McAleer, John Lanier, Roy Fox +1
Finding approximate Nash equilibria in zero-sum imperfect-information games is challenging when the number of information states is large. Policy Space Response Oracles (PSRO) is a…