1.1k citations · 1.6k across the 13 of their papers we have counts for
7 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)…
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…
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…
Autocurricula and the Emergence of Innovation from Social Interaction: A Manifesto for Multi-Agent Intelligence Research
Joel Z. Leibo, Edward Hughes, Marc Lanctot +1
Evolution has produced a multi-scale mosaic of interacting adaptive units. Innovations arise when perturbations push parts of the system away from stable equilibria into new regime…
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…
-Rank: Multi-Agent Evaluation by Evolution
Shayegan Omidshafiei, Christos Papadimitriou, Georgios Piliouras +7
We introduce -Rank, a principled evolutionary dynamics methodology for the evaluation and ranking of agents in large-scale multi-agent interactions, grounded in a novel dynamica…