Agents
A Decision-Language Model (DLM) for Dynamic Restless Multi-Armed Bandit Tasks in Public Health Nikhil Behari MIT, Harvard University Edwin Zhang
RMAB environment, and (3) iterate on the generated reward functions using feedback from grounded RMAB simulations.
Social Motion Prediction with Cognitive Hierarchies
In this study, we strive to replicate this ability by addressing the social motion prediction problem.
Robust Multi-Agent Reinforcement Learning via Adversarial Regularization: Theoretical Foundation and Stable Algorithms Alexander Bukharin Y an Li Yue Y u
However, ERNIE's adversarial regularization may introduce some training instability.
Combinatorial Group Testing with Selfish Agents
We study the Combinatorial Group Testing (CGT) problems in a novel game-theoretic framework, with a solution concept of Adversarial Equilibrium (AE).
On Tractable Φ-Equilibria in Non-Concave Games
V on Neumann's celebrated minimax theorem establishes the existence of Nash equilibrium in all two-player zero-sum games where the players' utilities are continuous as well as concave in their