Ego-centric Learning of Communicative World Models for Autonomous Driving

TL;DR

This paper introduces CALL, a novel multi-agent reinforcement learning framework for autonomous driving that uses generative AI and lightweight communication to improve world modeling, prediction accuracy, and planning in complex environments.

Contribution

The paper proposes CALL, a new approach combining generative AI-based world models with ego-centric learning and lightweight communication for scalable multi-agent autonomous driving.

Findings

Enhanced prediction accuracy through information sharing.

Improved planning performance in CARLA simulations.

Scalability benefits from low-dimensional latent representations.

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 \textit{partial observability} and \textit{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. By making use of generative AI embodied in world model together with its latent representation, we develop {\it CALL}, \underline{C}ommunic\underline{a}tive Wor\underline{l}d Mode\underline{l}, for MARL, where 1) each agent first learns its world model that encodes its state and intention into low-dimensional latent representation with smaller memory footprint, 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, and then exploits its generalization capacity to 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 \textit{CALL}.