G-Learning: Taming the Noise in Reinforcement Learning via Soft Updates

By Roy Fox, Ari Pakman, Naftali Tishby. 2015

16 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. Ofir Nachum
3. Pieter Abbeel
4. Mohammad Norouzi
5. Dale Schuurmans
6. Chelsea Finn
7. Yuxi Li
8. Shimon Whiteson

Citations in the corpus

• Trust-PCL: An Off-Policy Trust Region Method for Continuous Control (2017, methodology): “…previous work has used the relative entropy ( i.e., KL divergence) to regularize policy optimization; resulting in a softmax relationship between the optimal policy and state values (Peters et al., 2010; Azar et al., 2012, 2011; Fox et al., 2016; Rawlik et al., 2013) under single-step rollouts.”
• Information asymmetry in KL-regularized RL (2019, background): “Many works that consider the KL regularized objective either employ a simple or fixed default policy or directly work with the entropy formulation (e.g. Rubin et al., 2012; Fox et al., 2015; Haarnoja et al., 2017; Hausman et al., 2018).”
• Lipschitz Continuity in Model-based Reinforcement Learning (2018, background): “1 was proven by Littman & Szepesv´ari (1996), and 2 is proven several times (Fox et al., 2016; Asadi & Littman, 2017; Nachum et al., 2017; Neu et al., 2017).”