From the 1 of 8 linked papers with an AI index.
8 papers
Paradoxes of Game Theoretic Equilibria and Price of Anarchy
Georgios Piliouras, Ian Gemp, Siqi Liu +1
The paper shows that static equilibrium concepts like Nash and correlated equilibria can hide unstable dynamics in multi‑agent learning, leading to unbounded or chaotic inefficienc…
Combining Tree-Search, Generative Models, and Nash Bargaining Concepts in Game-Theoretic Reinforcement Learning
Zun Li, Marc Lanctot, Kevin R. McKee +7
Opponent modeling methods typically involve two crucial steps: building a belief distribution over opponents' strategies, and exploiting this opponent model by playing a best respo…
Deep Incentive Design with Differentiable Equilibrium Blocks
Vinzenz Thoma, Georgios Piliouras, Luke Marris
Automated design of multi-agent interactions with desirable equilibrium outcomes is inherently difficult due to the computational hardness, non-uniqueness, and instability of the r…
Evaluating Agents using Social Choice Theory
Marc Lanctot, Kate Larson, Yoram Bachrach +6
We argue that many general evaluation problems can be viewed through the lens of voting theory. Each task is interpreted as a separate voter, which requires only ordinal rankings o…
Convex Markov Games: A New Frontier for Multi-Agent Reinforcement Learning
Ian Gemp, Andreas Haupt, Luke Marris +2
Behavioral diversity, expert imitation, fairness, safety goals and others give rise to preferences in sequential decision making domains that do not decompose additively across tim…
Re-evaluating Open-ended Evaluation of Large Language Models
Siqi Liu, Ian Gemp, Luke Marris +3
Evaluation has traditionally focused on ranking candidates for a specific skill. Modern generalist models, such as Large Language Models (LLMs), decidedly outpace this paradigm. Op…