Article-Journal

This work designs and analyzes a novel set of algorithms for multi-agent reinforcement learning (MARL) based on the principle of information-directed sampling (IDS).

we propose a novel world model for MARL that learns decentralized local dynamics for scalability, combined with a centralized representation aggregation from all agents.

We give a comprehensive survey for embodied AI driven by large-scale models.

This paper proposes an offline Wasserstein-based approach to estimate the joint distribution of multivariate discounted cumulative rewards, establishes finite sample error bounds in the batch setting, and demonstrates its superior performance through extensive numerical studies.