Yao Mu

EUCLID: Towards Efficient Unsupervised Reinforcement Learning with Multi-choice Dynamics Model

2022 · arXiv (Cornell University)

Unsupervised reinforcement learning (URL) poses a promising paradigm to learn useful behaviors in a task-agnostic environment without the guidance of extrinsic rewards to facilitate the fast adaptation of various downstream tasks. Previous works focused on …

AdaptDiffuser: Diffusion Models as Adaptive Self-evolving Planners

2023 · arXiv (Cornell University)

Diffusion models have demonstrated their powerful generative capability in many tasks, with great potential to serve as a paradigm for offline reinforcement learning. However, the quality of the diffusion model is limited by the insufficient …