Yanbing Mao

2 accepted papers

2025

Real-DRL: Teach and Learn in Reality

NeurIPS 2025poster

This paper introduces the Real-DRL framework for safety-critical autonomous systems, enabling runtime learning of a deep reinforcement learning (DRL) agent to develop safe and high-performance action policies in real plants while prioritizing safety. The Real-DRL consists of three interactive compon…

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2024

Physics-Regulated Deep Reinforcement Learning: Invariant Embeddings

Hongpeng Cao, Yanbing Mao, Lui Sha, Marco Caccamo

ICLR 2024spotlight

This paper proposes the Phy-DRL: a physics-regulated deep reinforcement learning (DRL) framework for safety-critical autonomous systems. The Phy-DRL has three distinguished invariant-embedding designs: i) residual action policy (i.e., integrating data-driven-DRL action policy and physics-model-based…

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