7 papers
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…
Continuous World Coverage Path Planning for Fixed-Wing UAVs using Deep Reinforcement Learning
Mirco Theile, Andres R. Zapata Rodriguez, Marco Caccamo +1
Unmanned Aerial Vehicle (UAV) Coverage Path Planning (CPP) is critical for applications such as precision agriculture and search and rescue. While traditional methods rely on discr…
Runtime Learning of Quadruped Robots in Wild Environments
Yihao Cai, Yanbing Mao, Lui Sha +2
This paper presents a runtime learning framework for quadruped robots, enabling them to learn and adapt safely in dynamic wild environments. The framework integrates sensing, navig…
Physics-model-guided Worst-case Sampling for Safe Reinforcement Learning
Hongpeng Cao, Yanbing Mao, Lui Sha +1
Real-world accidents in learning-enabled CPS frequently occur in challenging corner cases. During the training of deep reinforcement learning (DRL) policy, the standard setup for t…
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…