Search Results for author: John Lanier

Found 5 papers, 2 papers with code

Anytime PSRO for Two-Player Zero-Sum Games

no code implementations19 Jan 2022 Stephen Mcaleer, Kevin Wang, John Lanier, Marc Lanctot, Pierre Baldi, Tuomas Sandholm, Roy Fox

PSRO is based on the tabular double oracle (DO) method, an algorithm that is guaranteed to converge to a Nash equilibrium, but may increase exploitability from one iteration to the next.

Multi-agent Reinforcement Learning reinforcement-learning

Improving Social Welfare While Preserving Autonomy via a Pareto Mediator

no code implementations7 Jun 2021 Stephen Mcaleer, John Lanier, Michael Dennis, Pierre Baldi, Roy Fox

Machine learning algorithms often make decisions on behalf of agents with varied and sometimes conflicting interests.

XDO: A Double Oracle Algorithm for Extensive-Form Games

1 code implementation NeurIPS 2021 Stephen Mcaleer, John Lanier, Kevin Wang, Pierre Baldi, Roy Fox

NXDO is the first deep RL method that can find an approximate Nash equilibrium in high-dimensional continuous-action sequential games.

Pipeline PSRO: A Scalable Approach for Finding Approximate Nash Equilibria in Large Games

2 code implementations NeurIPS 2020 Stephen McAleer, John Lanier, Roy Fox, Pierre Baldi

We also introduce an open-source environment for Barrage Stratego, a variant of Stratego with an approximate game tree complexity of $10^{50}$.

reinforcement-learning

ColosseumRL: A Framework for Multiagent Reinforcement Learning in $N$-Player Games

no code implementations10 Dec 2019 Alexander Shmakov, John Lanier, Stephen Mcaleer, Rohan Achar, Cristina Lopes, Pierre Baldi

Much of recent success in multiagent reinforcement learning has been in two-player zero-sum games.

Multiagent Systems

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