21 citations · 39 across the 4 of their papers we have counts for
3 papers · 1 filter
A Geometric Perspective on Self-Supervised Policy Adaptation
Cristian Bodnar, Karol Hausman, Gabriel Dulac-Arnold +1
One of the most challenging aspects of real-world reinforcement learning (RL) is the multitude of unpredictable and ever-changing distractions that could divert an agent from what…
Towards Differentiable Resampling
Michael Zhu, Kevin Murphy, Rico Jonschkowski
Resampling is a key component of sample-based recursive state estimation in particle filters. Recent work explores differentiable particle filters for end-to-end learning. However,…
Differentiable Particle Filters: End-to-End Learning with Algorithmic Priors
Rico Jonschkowski, Divyam Rastogi, Oliver Brock
We present differentiable particle filters (DPFs): a differentiable implementation of the particle filter algorithm with learnable motion and measurement models. Since DPFs are end…