6 papers · 1 filter
From Reward-Free Representations to Preferences: Rethinking Offline Preference-Based Reinforcement Learning
Jun-Jie Yang, Chia-Heng Hsu, Kui-Yuan Chen +1
Preference-based reinforcement learning (PbRL) avoids explicit reward engineering by learning from pairwise human preference feedback. Existing offline PbRL methods typically follo…
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…
Semi-Supervised Cross-Domain Imitation Learning
Li-Min Chu, Kai-Siang Ma, Ming-Hong Chen +1
Cross-domain imitation learning (CDIL) accelerates policy learning by transferring expert knowledge across domains, which is valuable in applications where the collection of expert…
Diminishing Exploration: A Minimalist Approach to Piecewise Stationary Multi-Armed Bandits
Kuan-Ta Li, Ping-Chun Hsieh, Yu-Chih Huang
The piecewise-stationary bandit problem is an important variant of the multi-armed bandit problem that further considers abrupt changes in the reward distributions. The main theme…
PPO-Clip Attains Global Optimality: Towards Deeper Understandings of Clipping
Nai-Chieh Huang, Ping-Chun Hsieh, Kuo-Hao Ho +1
Proximal Policy Optimization algorithm employing a clipped surrogate objective (PPO-Clip) is a prominent exemplar of the policy optimization methods. However, despite its remarkabl…
Accelerated Policy Gradient: On the Convergence Rates of the Nesterov Momentum for Reinforcement Learning
Yen-Ju Chen, Nai-Chieh Huang, Ching-Pei Lee +1
Various acceleration approaches for Policy Gradient (PG) have been analyzed within the realm of Reinforcement Learning (RL). However, the theoretical understanding of the widely us…