Non-parametric Policy Search with Limited Information Loss

By Herke Van Hoof, Gerhard Neumann, Jan Peters. 2017

27 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. Sergey Levine
2. Scott Fujimoto
3. Aviral Kumar
4. Pieter Abbeel
5. Ofir Nachum
6. Doina Precup
7. Shixiang Shane Gu
8. David Meger

Citations in the corpus

• Off-Policy Deep Reinforcement Learning without Exploration (2018, methodology): “…connects to policy methods with conservative updates (Kakade & Langford, 2002), such as trust region (Schulman et al., 2015; Achiam et al., 2017) and information-theoretic methods (Peters & M ¨ ulling, 2010; Van Hoof et al., 2017) which aim to keep an updated policy similar to the previous policy.”
• Learning Robust Policies for Object Manipulation with Robot Swarms (2018, methodology): “With these weights, π̃ can be approximated with a weighted Gaussian Process (GP) [17], where the action ai for state si is weighted by wi.”