8 accepted papers
Reinforcement learning (RL) policies represented in Reproducing Kernel Hilbert Spaces (RKHS) offer powerful representational capabilities. While second-order optimization methods like Newton's method demonstrate faster convergence than first-order approaches, current RKHS-based policy optimization r…
Offline reinforcement learning faces a critical challenge in synthesizing high-reward trajectories from suboptimal datasets while robustly handling the stochasticity inherent in real-world decision-making. While combination of return-conditioned sequence models, such as Decision Transformers (DT), a
Value decomposition methods are effective for multi-agent reinforcement learning (MARL), with QMIX being one of the most advanced. However, it struggles with scalability and flexibility in large-scale agent systems. The introduction of mean-field theory into MARL provides a potential solution to bot…
Continuous Domain Adaptation (CDA) effectively bridges significant domain shifts by progressively adapting from the source domain through intermediate domains to the target domain. However, selecting intermediate domains without explicit metadata remains a substantial challenge that has not been ext…
Achieving optimal performance in reinforcement learning requires robust policies supported by training processes that ensure both sample efficiency and stability. Modeling the policy in reproducing kernel Hilbert space (RKHS) enables efficient exploration of local optimal solutions. However, the sta…
Mean-field theory offers a promising solution to the scalability issues encountered in multi-agent reinforcement learning (MARL) within large-scale systems. However, most existing MARL algorithms based on mean-field theory are typically constrained to discrete action space. In continuous action spac…
The learning approaches of designing a controller to guide the collective behavior of swarm robots have gained significant attention in recent years. However, the scalability of swarm robots and their inherent stochasticity complicate the control problem due to increasing complexity, unpredictabilit…
Vibration perception is essential for robotic sensing and dynamic control. Nevertheless, due to the rigorous demand for sensor conformability and stretchability, enabling soft robots with proprioceptive vibration sensing remains challenging. This paper proposes a novel liquid metal-based stretchable…