16 citations · 19 across the 7 of their papers we have counts for
12 papers
Game-Theoretical Perspectives on Active Equilibria: A Preferred Solution Concept over Nash Equilibria
Dong-Ki Kim, Matthew Riemer, Miao Liu +3
Multiagent learning settings are inherently more difficult than single-agent learning because each agent interacts with other simultaneously learning agents in a shared environment…
Proximal Learning With Opponent-Learning Awareness
Stephen Zhao, Chris Lu, Roger Baker Grosse +1
Learning With Opponent-Learning Awareness (LOLA) (Foerster et al. [2018a]) is a multi-agent reinforcement learning algorithm that typically learns reciprocity-based cooperation in…
Discovered Policy Optimisation
Chris Lu, Jakub Grudzien Kuba, Alistair Letcher +3
Tremendous progress has been made in reinforcement learning (RL) over the past decade. Most of these advancements came through the continual development of new algorithms, which we…
Human-AI Coordination via Human-Regularized Search and Learning
Hengyuan Hu, David J Wu, Adam Lerer +2
We consider the problem of making AI agents that collaborate well with humans in partially observable fully cooperative environments given datasets of human behavior. Inspired by p…
An Investigation of the Bias-Variance Tradeoff in Meta-Gradients
Risto Vuorio, Jacob Beck, Shimon Whiteson +2
Meta-gradients provide a general approach for optimizing the meta-parameters of reinforcement learning (RL) algorithms. Estimation of meta-gradients is central to the performance o…
Learned Belief Search: Efficiently Improving Policies in Partially Observable Settings
Hengyuan Hu, Adam Lerer, Noam Brown +1
Search is an important tool for computing effective policies in single- and multi-agent environments, and has been crucial for achieving superhuman performance in several benchmark…