142 citations · 299 across the 8 of their papers we have counts for
8 papers · 1 filter
A Generalized Training Approach for Multiagent Learning
Paul Muller, Shayegan Omidshafiei, Mark Rowland +12
This paper investigates a population-based training regime based on game-theoretic principles called Policy-Spaced Response Oracles (PSRO). PSRO is general in the sense that it (1)…
Multiagent Evaluation under Incomplete Information
Mark Rowland, Shayegan Omidshafiei, Karl Tuyls +4
This paper investigates the evaluation of learned multiagent strategies in the incomplete information setting, which plays a critical role in ranking and training of agents. Tradit…
OpenSpiel: A Framework for Reinforcement Learning in Games
Marc Lanctot, Edward Lockhart, Jean-Baptiste Lespiau +24
OpenSpiel is a collection of environments and algorithms for research in general reinforcement learning and search/planning in games. OpenSpiel supports n-player (single- and multi…
Foolproof Cooperative Learning
Alexis Jacq, Julien Perolat, Matthieu Geist +1
This paper extends the notion of learning equilibrium in game theory from matrix games to stochastic games. We introduce Foolproof Cooperative Learning (FCL), an algorithm that con…
Neural Replicator Dynamics
Daniel Hennes, Dustin Morrill, Shayegan Omidshafiei +8
Policy gradient and actor-critic algorithms form the basis of many commonly used training techniques in deep reinforcement learning. Using these algorithms in multiagent environmen…
Computing Approximate Equilibria in Sequential Adversarial Games by Exploitability Descent
Edward Lockhart, Marc Lanctot, Julien Pérolat +4
In this paper, we present exploitability descent, a new algorithm to compute approximate equilibria in two-player zero-sum extensive-form games with imperfect information, by direc…