Reinforcement Learning
Uncoupled and Convergent Learning in Two-Player Zero-Sum Markov Games with Bandit Feedback
Our paper takes the first step to remove such assumptions.
Learning to Influence Human Behavior with Offline Reinforcement Learning
When interacting with people, AI agents do not just influence the state of the world - they also influence the actions people take in response to the agent, and even their underlying intentions and strategies.
Geometric Algebra Transformer
Such data can take numerous forms, for instance points, direction vectors, translations, or rotations, but to date there is no single architecture that can be applied to such a wide variety of geometric types while respecting their symmetries. In this paper we introduce the Geometric Algebra Transformer (GA Tr), a general-purpose architecture for geometric data.
Mixed-Initiative Multiagent Apprenticeship Learning for Human Training of Robot Teams
The key ingredient to MixTURE's success is automatically learning
Regret Minimization via Saddle Point Optimization
A long line of works characterizes the sample complexity of regret minimization in sequential decision-making by min-max programs. In the corresponding saddle-point game, the min-player optimizes the sampling distribution against an adversarial max-player that chooses confusing models leading to large regret.
