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Found 5 papers, 2 papers with code
Pipeline PSRO: A Scalable Approach for Finding Approximate Nash Equilibria in Large Games
We also introduce an open-source environment for Barrage Stratego, a variant of Stratego with an approximate game tree complexity of $10^{50}$.
XDO: A Double Oracle Algorithm for Extensive-Form Games
NXDO is the first deep RL method that can find an approximate Nash equilibrium in high-dimensional continuous-action sequential games.
ColosseumRL: A Framework for Multiagent Reinforcement Learning in $N$-Player Games
Much of recent success in multiagent reinforcement learning has been in two-player zero-sum games.
Multiagent Systems
Improving Social Welfare While Preserving Autonomy via a Pareto Mediator
Machine learning algorithms often make decisions on behalf of agents with varied and sometimes conflicting interests.
Anytime PSRO for Two-Player Zero-Sum Games
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
+2
