Article-Journal

Our work builds upon the investigation of successor representations for task generalization in online RL and extends the framework to incorporate offline-to-online learning.

We propose the Robust Offline-to-Online (RO2O) algorithm, designed to enhance offline policies through uncertainty and smoothness, and to mitigate the performance drop in online adaptation.

We propose a novel Dynamic Skill Learning (DSL) framework to enable more effective adaptation and collaboration in complex tasks.

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.