Recently, the field of generative models has seen a significant advancement with the
introduction of Diffusion Probabilistic Models (DPMs). The Denoising Diffusion Implicit Model …

Multi-objective reinforcement learning (MORL) has been proposed to learn control policies
over multiple competing objectives with each possible preference over returns. However …

Imitation learning from human feedback studies how to train well-performed imitation agents
with an annotator's relative comparison of two demonstrations (one demonstration is …

Imitation learning has emerged as a promising approach for addressing sequential decision-
making problems, with the assumption that expert demonstrations are optimal. However, in …

Deep reinforcement learning (DRL) has achieved remarkable milestones in the field of
artificial intelligence. However, the reward functions for most real-world tasks are …

Achieving carbon neutrality within industrial operations has become increasingly imperative
for sustainable development. It is both a significant challenge and a key opportunity for …

Adversarial Imitation Learning (AIL) allows the agent to reproduce expert behavior with low-
dimensional states and actions. However, challenges arise in handling visual states due to …

In imitation learning, it is often assumed the demonstration data are optimal, even though
they are imperfect in practice. The imperfect demonstrations result from expert errors, large …