Aug 9, 2018 · We place policy optimization into the space of probability measures, and interpret it as Wasserstein gradient flows. On the probability-measure ...

We place policy optimization into the space of probability measures, and interpret it as Wasserstein gradient flows. On the probability-measure space, under ...

In this paper we introduce gradient flows in the space of probability distributions, called Wasserstein gradient flows. (WGF), and formulate policy optimization ...

Policy optimization is a core component of rein- forcement learning (RL), and most existing RL methods directly optimize parameters of a policy.

May 17, 2023 · We focus on robot motion policies that build on Gaussian mixture models (GMMs) and formulate the policy optimization as a Wassertein gradient flow over the ...

This work places policy optimization into the space of probability measures, and interpret it as Wasserstein gradient flows, and develops efficient ...

Policy gradients methods often achieve better performance when the change in policy is limited to a small Kullback-Leibler divergence.

Specifically, we focus on robot motion policies that build on Gaussian mixture models (GMMs) and formulate the policy optimization as a Wasserstein gradient ...

Aug 11, 2018 · We place policy optimization into the space of probability measures, and interpret it as Wasserstein gradient flows. On the probability-measure ...

On this basis, we get a large-scale Wasserstein gradient flow RL method by introducing input convex neural networks (ICNNs) to improve the Jordan-Kinderlehrer- ...