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Blending Data-Driven Priors in Dynamic Games
As intelligent robots like autonomous vehicles become increasingly deployed in the presence of people, the extent to which these systems should leverage model-based game-theoretic planners versus data-driven policies for safe, interaction-aware motion planning remains an open question.
NashFormer: Leveraging Local Nash Equilibria for Semantically Diverse Trajectory Prediction
Interactions between road agents present a significant challenge in trajectory prediction, especially in cases involving multiple agents.
Provably Efficient Multi-Agent Reinforcement Learning with Fully Decentralized Communication
Distributed exploration reduces sampling complexity in multi-agent RL (MARL).
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