831 citations · 3.2k across the 65 of their papers we have counts for
3 papers · 1 filter
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…