4 papers
A Reward-Free Viewpoint on Multi-Objective Reinforcement Learning
Ying-Tu Chen, Wei Hung, Bing-Shu Wu +2
Many sequential decision-making tasks involve optimizing multiple conflicting objectives, requiring policies that adapt to different user preferences. In multi-objective reinforcem…
Action-Constrained Imitation Learning
Chia-Han Yeh, Tse-Sheng Nan, Risto Vuorio +4
Policy learning under action constraints plays a central role in ensuring safe behaviors in various robot control and resource allocation applications. In this paper, we study a ne…
Efficient Action-Constrained Reinforcement Learning via Acceptance-Rejection Method and Augmented MDPs
Wei Hung, Shao-Hua Sun, Ping-Chun Hsieh
Action-constrained reinforcement learning (ACRL) is a generic framework for learning control policies with zero action constraint violation, which is required by various safety-cri…
Enhancing Offline Model-Based RL via Active Model Selection: A Bayesian Optimization Perspective
Yu-Wei Yang, Yun-Ming Chan, Wei Hung +2
Offline model-based reinforcement learning (MBRL) serves as a competitive framework that can learn well-performing policies solely from pre-collected data with the help of learned…