Ego-centric Learning of Communicative World Models for Autonomous Driving

ICLR 2025 Conference Submission12264 Authors

Keywords: World Model, Reinforcement Learning, Autonomous Driving, Distributed Learning

Abstract:

We study multi-agent reinforcement learning (MARL) for tasks in complex high-dimensional environments, such as autonomous driving.
MARL is known to suffer from the *partial observability* and *non-stationarity* issues. To tackle these challenges, information sharing is often employed, which however faces major hurdles in practice, including overwhelming communication overhead and scalability concerns. Based on the key observation that world model encodes high-dimensional inputs to low-dimensional latent representation with a small memory footprint, we develop *CALL*, {C}ommunic{a}tive Wor{l}d Mode{l}, for ego-centric MARL, where 1) each agent
first learns its world model that encodes its state and intention into low-dimensional latent representation which can be shared with other agents of interest via lightweight communication; and 2) each agent carries out ego-centric learning while exploiting lightweight information sharing to enrich her world model learning and improve prediction for better planning. We characterize the gain on the prediction accuracy from the information sharing and its impact on performance gap. Extensive experiments are carried out on the challenging local trajectory planning tasks in the CARLA platform to demonstrate the performance gains of using *CALL*.

Primary Area: reinforcement learning

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Submission Number: 12264