831 citations · 3.2k across the 65 of their papers we have counts for
68 papers
Who Needs to Know? Minimal Knowledge for Optimal Coordination
Niklas Lauffer, Ameesh Shah, Micah Carroll +2
To optimally coordinate with others in cooperative games, it is often crucial to have information about one's collaborators: successful driving requires understanding which side of…
Bridging RL Theory and Practice with the Effective Horizon
Cassidy Laidlaw, Stuart Russell, Anca Dragan
Deep reinforcement learning (RL) works impressively in some environments and fails catastrophically in others. Ideally, RL theory should be able to provide an understanding of why…
Active Reward Learning from Multiple Teachers
Peter Barnett, Rachel Freedman, Justin Svegliato +1
Reward learning algorithms utilize human feedback to infer a reward function, which is then used to train an AI system. This human feedback is often a preference comparison, in whi…
imitation: Clean Imitation Learning Implementations
Adam Gleave, Mohammad Taufeeque, Juan Rocamonde +7
imitation provides open-source implementations of imitation and reward learning algorithms in PyTorch. We include three inverse reinforcement learning (IRL) algorithms, three imita…
Optimal Conservative Offline RL with General Function Approximation via Augmented Lagrangian
Paria Rashidinejad, Hanlin Zhu, Kunhe Yang +2
Offline reinforcement learning (RL), which refers to decision-making from a previously-collected dataset of interactions, has received significant attention over the past years. Mu…
Cooperative and uncooperative institution designs: Surprises and problems in open-source game theory
Andrew Critch, Michael Dennis, Stuart Russell
It is increasingly possible for real-world agents, such as software-based agents or human institutions, to view the internal programming of other such agents that they interact wit…