On Reinforcement Learning of Control Actions in Noisy and Non-Markovian Domains

By Mark D. Pendrith. 1994

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. Roy Fox
2. Ari Pakman
3. Risto Miikkulainen
4. Naftali Tishby
5. Kenneth O. Stanley
6. Pawea Cichosz
7. Sergey Levine
8. Pieter Abbeel

Citations in the corpus

• Taming the Noise in Reinforcement Learning via Soft Updates (2015, background): “For other works that study the effect of noise in Q-learning, although without identifying the bias (7), see [26, 27, 28].”
• Truncating Temporal Diierences: on the Eecient Implementation of Td for Reinforcement Learning (1995, background): “Other related work is that of Pendrith 1994.”
• Evolving Neural Networks through Augmenting Topologies (2002, methodology): “Standard reinforcement learning methods have also been applied to this task (Anderson, 1989; Pendrith, 1994).”
• Efficient reinforcement learning through symbiotic evolution (2004, unknown): “Our Q-learning simulations were run using the simulation code developed by Pendrith (1994), which employs one-step updates as described by Watkins and Dayan (1992).”