On The Virtues of Linear Learning and Trajectory Distributions

By Richard S. Sutton. 2007

8 citations recorded by the source corpus.

Subfields: Probabilistic & Bayesian Methods, Statistical Learning Theory

How researchers see this paper

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

In Probabilistic & Bayesian Methods

1. George Tucker
2. Yifan Wu
3. Florian Strub
4. Matteo Hessel
5. Ofir Nachum
6. Hado van Hasselt
7. John Aslanides

In Statistical Learning Theory

1. Matteo Hessel
2. John Aslanides
3. Hado van Hasselt
4. George Tucker
5. Yifan Wu
6. Ofir Nachum
7. Huizhen Yu
8. Pieter Abbeel

Citations in the corpus

• When to use parametric models in reinforcement learning? (2019, background): “, 2000) can be unstable (Williams and Baird III, 1993; Baird, 1995; Sutton, 1995; Tsitsiklis and Van Roy, 1997; Sutton et al., 2009, 2016) — this is sometimes called the deadly triad (Sutton and Barto, 2018; van Hasselt et al.”
• Behavior Regularized Offline Reinforcement Learning (2019, methodology): “These failure cases are hypothesized to be caused by erroneous generalization of the state-action value function (Q-value function) learned with function approximators, as suggested by Sutton (1995); Baird (1995); Tsitsiklis & Van Roy (1997); Van Hasselt et al. (2018).”
• Deep Reinforcement Learning and the Deadly Triad (2018, background): “This can create harmful learning dynamics that can lead to divergence of the function parameters (Sutton, 1995; Baird, 1995; Tsitsiklis and Van Roy, 1997).”