Agents
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
Safety through feedback in Constrained RL
This feedback can be system generated or elicited from a human observing the training process. Previous approaches have not been able to scale to complex environments and are constrained to receiving feedback at the state level which can be expensive to collect. To this end, we introduce an approach that scales to more complex domains and extends beyond state-level feedback, thus, reducing the burden on the evaluator.