3 citations · 7 across the 18 of their papers we have counts for
5 papers · 2 filters
Reinforcement Learning via Fixed-point Flow Matching
Denis Blessing, Onur Celik, Gerhard Neumann
In maximum entropy and behavior-constrained reinforcement learning (RL), the optimal policy is given by a Boltzmann distribution. Generating samples from this distribution can be c…
Fixed-point neural samplers on discrete spaces
Jiajun He, Denis Blessing, Mouyang Cheng +2
Sampling from discrete, unnormalized distributions without access to data is a challenging problem. Neural samplers offer a promising approach by training generative models from de…
Scalable Maximum Entropy Reinforcement Learning for Diffusion Policies via Adjoint Matching
Serge Thilges, Onur Celik, Denis Blessing +2
Diffusion policies have recently emerged as a powerful paradigm for representing complex action distributions in reinforcement learning (RL). However, their application to online R…
Trust-Region Diffusion Policies for Massively Parallel On-Policy RL
Huy Le, Onur Celik, Denis Blessing +6
Reinforcement learning with massively parallel simulations has become a standard framework for developing robust, deployable policies; however, most existing approaches still rely…
Bridge Matching Sampler: Scalable Sampling via Generalized Fixed-Point Diffusion Matching
Denis Blessing, Lorenz Richter, Julius Berner +2
Sampling from unnormalized densities using diffusion models has emerged as a powerful paradigm. However, while recent approaches that use least-squares `matching' objectives have i…