428 citations · 469 across the 13 of their papers we have counts for
13 papers
Hierarchical Behaviour Spaces
Michael Tryfan Matthews, Anssi Kanervisto, Jakob Foerster +3
Recent work in hierarchical reinforcement learning has shown success in scaling to billions of timesteps when learning over a set of predefined option reward functions. We show tha…
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…
Quasi-Equivalence Discovery for Zero-Shot Emergent Communication
Kalesha Bullard, Douwe Kiela, Franziska Meier +2
Effective communication is an important skill for enabling information exchange in multi-agent settings and emergent communication is now a vibrant field of research, with common s…
Off-Belief Learning
Hengyuan Hu, Adam Lerer, Brandon Cui +4
The standard problem setting in Dec-POMDPs is self-play, where the goal is to find a set of policies that play optimally together. Policies learned through self-play may adopt arbi…
Ridge Rider: Finding Diverse Solutions by Following Eigenvectors of the Hessian
Jack Parker-Holder, Luke Metz, Cinjon Resnick +6
Over the last decade, a single algorithm has changed many facets of our lives - Stochastic Gradient Descent (SGD). In the era of ever decreasing loss functions, SGD and its various…
The Struggles of Feature-Based Explanations: Shapley Values vs. Minimal Sufficient Subsets
Oana-Maria Camburu, Eleonora Giunchiglia, Jakob Foerster +2
For neural models to garner widespread public trust and ensure fairness, we must have human-intelligible explanations for their predictions. Recently, an increasing number of works…
Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning
Tabish Rashid, Mikayel Samvelyan, Christian Schroeder de Witt +3
In many real-world settings, a team of agents must coordinate its behaviour while acting in a decentralised fashion. At the same time, it is often possible to train the agents in a…
Robust Visual Domain Randomization for Reinforcement Learning
Reda Bahi Slaoui, William R. Clements, Jakob N. Foerster +1
Producing agents that can generalize to a wide range of visually different environments is a significant challenge in reinforcement learning. One method for overcoming this issue i…