6 citations · 7 across the 11 of their papers we have counts for
6 papers · 1 filter
Safe Online Learning via Smooth Safety-Structured Policy Composition
Hongpeng Cao, Liqun Zhao, Yuliang Gu +3
Safe online reinforcement learning requires policies to respect safety constraints while maintaining smooth optimization dynamics. Existing approaches typically rely on either stri…
Observations Meet Actions: Learning Control-Sufficient Representations for Robust Policy Generalization
Yuliang Gu, Hongpeng Cao, Marco Caccamo +1
Capturing latent variations ("contexts") is key to deploying reinforcement-learning (RL) agents beyond their training regime. We recast context-based RL as a dual inference-control…
Bregman Centroid Guided Cross-Entropy Method
Yuliang Gu, Hongpeng Cao, Marco Caccamo +1
The Cross-Entropy Method (CEM) is a widely adopted trajectory optimizer in model-based reinforcement learning (MBRL), but its unimodal sampling strategy often leads to premature co…
Action Mapping for Reinforcement Learning in Continuous Environments with Constraints
Mirco Theile, Lukas Dirnberger, Raphael Trumpp +2
Deep reinforcement learning (DRL) has had success across various domains, but applying it to environments with constraints remains challenging due to poor sample efficiency and slo…
Simplex-enabled Safe Continual Learning Machine
Hongpeng Cao, Yanbing Mao, Yihao Cai +2
This paper proposes the SeC-Learning Machine: Simplex-enabled safe continual learning for safety-critical autonomous systems. The SeC-learning machine is built on Simplex logic (th…
Equivariant Ensembles and Regularization for Reinforcement Learning in Map-based Path Planning
Mirco Theile, Hongpeng Cao, Marco Caccamo +1
In reinforcement learning (RL), exploiting environmental symmetries can significantly enhance efficiency, robustness, and performance. However, ensuring that the deep RL policy and…