Reinforcement learning from human feedback (RLHF) is a variant of reinforcement learning
(RL) that learns from human feedback instead of relying on an engineered reward function …

Recent offline meta-reinforcement learning (meta-RL) methods typically utilize task-
dependent behavior policies (eg, training RL agents on each individual task) to collect a …

Generating complex behaviors that satisfy the preferences of non-expert users is a crucial
requirement for AI agents. Interactive reward learning from trajectory comparisons (aka …

Deep reinforcement learning has shown promising application effects in many fields.
However, issues such as low sample efficiency and weak knowledge transfer and …

Meta-Reinforcement Learning (MRL) is a promising framework for training agents that can
quickly adapt to new environments and tasks. In this work, we study the MRL problem under …

The fine spectral information contained in hyperspectral images (HSI) is combined with rich
spatial features to provide feature qualities that serve as distinguishing variables for efficient …

Reinforcement Learning (RL) has become a critical tool for optimization challenges within
automation, leading to significant advancements in several areas. This review article …

Extensive efforts have been dedicated to the development of AI agents that can
independently carry out sequential decision-making tasks. Learning-based solutions …

This thesis studies collaborative learning framework, where a group of agents cooperate to
learn a powerful model from their local data in a distributed or decentralized manner. We …

Constraint learning from human feedback (CLHF) has garnered significant interest in the
domain of safe reinforcement learning (RL) due to the challenges associated with designing …