12 citations · 12 across the 7 of their papers we have counts for
7 papers
Beyond Isolation: Unlocking Reinforcement Learning Component Synergy for Sample-Efficient Continuous Control
Qi Zhao, Guozheng Ma, Yilun Kong +9
Reinforcement learning systems are significantly more complex than other machine learning paradigms due to inherent properties, causing RL system design to jointly account for many…
Co-Evolutionary Multi-Modal Alignment via Structured Adversarial Evolution
Guoxin Shi, Haoyu Wang, Zaihui Yang +2
Adversarial behavior plays a central role in aligning large language models with human values. However, existing alignment methods largely rely on static adversarial settings, whic…
UACER: An Uncertainty-Adaptive Critic Ensemble Framework for Robust Adversarial Reinforcement Learning
Jiaxi Wu, Tiantian Zhang, Yuxing Wang +2
Robust adversarial reinforcement learning has emerged as an effective paradigm for training agents to handle uncertain disturbance in real environments, with critical applications…
Embodied Co-Design for Rapidly Evolving Agents: Taxonomy, Frontiers, and Challenges
Yuxing Wang, Zhiyu Chen, Tiantian Zhang +5
Brain-body co-evolution enables animals to develop complex behaviors in their environments. Inspired by this biological synergy, embodied co-design (ECD) has emerged as a transform…
Morphology and Behavior Co-Optimization of Modular Satellites for Attitude Control
Yuxing Wang, Jie Li, Cong Yu +5
The emergence of modular satellites marks a significant transformation in spacecraft engineering, introducing a new paradigm of flexibility, resilience, and scalability in space ex…
SongDriver: Real-time Music Accompaniment Generation without Logical Latency nor Exposure Bias
Zihao Wang, Qihao Liang, Kejun Zhang +8
Real-time music accompaniment generation has a wide range of applications in the music industry, such as music education and live performances. However, automatic real-time music a…
Dynamics-Adaptive Continual Reinforcement Learning via Progressive Contextualization
Tiantian Zhang, Zichuan Lin, Yuxing Wang +7
A key challenge of continual reinforcement learning (CRL) in dynamic environments is to promptly adapt the RL agent's behavior as the environment changes over its lifetime, while m…