Uncertainty quantification (UQ) is essential for creating trustworthy machine learning
models. Recent years have seen a steep rise in UQ methods that can flag suspicious …

Offline reinforcement learning (RL) presents a promising approach for learning reinforced
policies from offline datasets without the need for costly or unsafe interactions with the …

Cross-domain offline reinforcement learning leverages source domain data with diverse
transition dynamics to alleviate the data requirement for the target domain. However, simply …

In reinforcement learning (RL), training a policy from scratch with online experiences can be
inefficient because of the difficulties in exploration. Recently, offline RL provides a promising …

Uncertainty quantification (UQ) is essential for creating trustworthy machine learning
models. Recent years have seen a steep rise in UQ methods that can flag suspicious …

Unsupervised Reinforcement Learning (RL) provides a promising paradigm for learning
useful behaviors via reward-free per-training. Existing methods for unsupervised RL mainly …

Offline reinforcement learning (RL) allows learning sequential behavior from fixed datasets.
Since offline datasets do not cover all possible situations, many methods collect additional …

Offline meta-reinforcement learning aims to equip agents with the ability to rapidly adapt to
new tasks by training on data from a set of different tasks. Context-based approaches utilize …