Normalizing flows provide a general mechanism for defining expressive probability
distributions, only requiring the specification of a (usually simple) base distribution and a …

Abstract Designing rewards for Reinforcement Learning (RL) is challenging because it
needs to convey the desired task, be efficient to optimize, and be easy to compute. The latter …

We introduce a simple new method for visual imitation learning, which allows a novel robot
manipulation task to be learned from a single human demonstration, without requiring any …

Deep generative models (DGMs) have demonstrated great success across various domains,
particularly in generating texts, images, and videos using models trained from offline data …

Learning from demonstrations can be challenging when the quality of demonstrations is
diverse, and even more so when the quality is unknown and there is no additional …

Imitation learning in a high-dimensional environment is challenging. Most inverse
reinforcement learning (IRL) methods fail to outperform the demonstrator in such a high …

In reinforcement learning and imitation learning, an object of central importance is the state
distribution induced by the policy. It plays a crucial role in the policy gradient theorem, and …

I consider two problems in machine learning and statistics: the problem of estimating the
joint probability density of a collection of random variables, known as density estimation, and …

Abstract Generative Adversarial Imitation Learning (GAIL) can learn policies without
explicitly defining the reward function from demonstrations. GAIL has the potential to learn …

We address the problem of credit assignment in reinforcement learning and explore
fundamental questions regarding the way in which an agent can best use additional …