Learning Gaussian Policies from Smoothed Action Value Functions

By Ofir Nachum, Mohammad Norouzi, George Tucker, Dale Schuurmans. 2018

3 citations recorded by the source corpus.

Subfields: Reinforcement Learning

How researchers see this paper

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

In Reinforcement Learning

1. Sergey Levine
2. Aviral Kumar
3. Xue Bin Peng
4. Chelsea Finn
5. Shixiang Shane Gu
6. Ofir Nachum
7. Grace Zhang
8. George Tucker

Citations in the corpus

• Advantage-Weighted Regression: Simple and Scalable Off-Policy Reinforcement Learning (2019, background): “But these methods can be notoriously unstable, and in practice, require a variety of stabilization techniques to ensure more consistent performance (Hasselt et al., 2016; Wang et al., 2016; Munos et al., 2016; Hessel et al., 2017; Fujimoto et al., 2018; Nachum et al., 2018; Fu et al., 2019).”
• Reward-Conditioned Policies (2019, background): “While such methods have attained impressive results on a range of challenging tasks [13, 20, 22, 28, 36], they are also known to be notoriously challenging to use effectively, due to sensitivity to hyperparameters, high sample complexity, and a range of important and delicate implementation choices ”