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cs.LG2023
NashFormer: Leveraging Local Nash Equilibria for Semantically Diverse Trajectory Prediction
Justin Lidard, Oswin So, Yanxia Zhang +8
Interactions between road agents present a significant challenge in trajectory prediction, especially in cases involving multiple agents. Because existing diversity-aware predictor…
cs.LG2021
Provably Efficient Multi-Agent Reinforcement Learning with Fully Decentralized Communication
Justin Lidard, Udari Madhushani, Naomi Ehrich Leonard
A challenge in reinforcement learning (RL) is minimizing the cost of sampling associated with exploration. Distributed exploration reduces sampling complexity in multi-agent RL (MA…