Diffusion models have demonstrated highly-expressive generative capabilities in vision and
NLP. Recent studies in reinforcement learning (RL) have shown that diffusion models are …

Imitation learning (IL), a burgeoning frontier in machine learning, holds immense promise
across diverse domains. In recent years, its integration into robotics has sparked significant …

In this paper, we present ContExtual Imitation Learning (CEIL), a general and broadly
applicable algorithm for imitation learning (IL). Inspired by the formulation of hindsight …

Offline imitation learning (IL) is a powerful method to solve decision-making problems from
expert demonstrations without reward labels. Existing offline IL methods suffer from severe …

We study the problem of representation transfer in offline Reinforcement Learning (RL),
where a learner has access to episodic data from a number of source tasks collected a …

Offline reinforcement learning (RL) Algorithms are often designed with environments such
as MuJoCo in mind, in which the planning horizon is extremely long and no noise exists. We …

Offline reinforcement learning (RL) methods learn from datasets without further environment
interaction, facing errors due to out-of-distribution (OOD) actions. Although effective methods …

Offline reinforcement learning algorithms still lack trust in practice due to the risk that the
learned policy performs worse than the original policy that generated the dataset or behaves …

In offline Imitation Learning (IL), one of the main challenges is the covariate shift between
the expert observations and the actual distribution encountered by the agent, because it is …

Offline reinforcement learning (RL) has emerged as a promising paradigm for real-world
applications since it aims to train policies directly from datasets of past interactions with the …