Improving Artificial Intelligence with Games

Two desiderata of reinforcement learning (RL) algorithms are the ability to learn from relatively little experience and the ability to learn policies that generalize to a range of problem specifications. In factored state spaces, one approach towards achieving both goals is …

Robustly cooperating with unseen agents and human partners presents significant challenges due to the diverse cooperative conventions these partners may adopt. Existing Ad Hoc Teamwork (AHT) methods address this challenge by training an agent with a population of diverse tea…

We consider algorithms for learning reward functions from human preferences over pairs of trajectory segments---as used in reinforcement learning from human feedback (RLHF)---including those used to fine tune ChatGPT and other contemporary language models. Most recent work o…