Balancing Multiple Sources of Reward in Reinforcement Learning

By Christian R. Shelton. 2000

51 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Management Information Systems

How researchers see this paper

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

In Artificial Intelligence

1. Peter Stone
2. Sergey Levine
3. Joelle Pineau
4. Pieter Abbeel
5. Lihong Li
6. Satinder Singh
7. Matthew E. Taylor
8. Yoshua Bengio

In Management Information Systems

1. Satinder Singh
2. Xin Xu
3. Chunming Liu
4. Dewen Hu
5. Peter Stone
6. Charles Lee Isbell Jr.
7. Michael Kearns
8. Joelle Pineau

Citations in the corpus

• Informing sequential clinical decision-making through reinforcement learning: an empirical study (2010, background): “problem (Bagnell et al. 2001; Shelton 2001; Doshi et al. 2008; Lizotte et al. 2010) but more”
• Multiobjective Reinforcement Learning: A Comprehensive Overview (2015, methodology): “Shelton [43] applied policy gradient methods and the idea of varying parameters to the MORL domain.”
• A social reinforcement learning agent (2001, methodology): “, 1999)’s policy gradient algorithm and its details are beyond the scope of this paper (however, see (Shelton, 2000) for details).”