- author = "Scott Niekum and Lee Spector and Andrew Barto",
- title = "Evolution of reward functions for reinforcement learning",
- booktitle = "GECCO '11: Proceedings of the 13th annual conference companion on Genetic and evolutionary computation",
- year = "2011",
- editor = "Natalio Krasnogor and Pier Luca Lanzi and Andries Engelbrecht and David Pelta and Carlos Gershenson and Giovanni Squillero and Alex Freitas and Marylyn Ritchie and Mike Preuss and Christian Gagne and Yew Soon Ong and Guenther Raidl and Marcus Gallager and Jose Lozano and Carlos Coello-Coello and Dario Landa Silva and Nikolaus Hansen and Silja Meyer-Nieberg and Jim Smith and Gus Eiben and Ester Bernado-Mansilla and Will Browne and Lee Spector and Tina Yu and Jeff Clune and Greg Hornby and Man-Leung Wong and Pierre Collet and Steve Gustafson and Jean-Paul Watson and Moshe Sipper and Simon Poulding and Gabriela Ochoa and Marc Schoenauer and Carsten Witt and Anne Auger",
- isbn13 = "978-1-4503-0690-4",
- keywords = "genetic algorithms, genetic programming, Genetics based machine learning: Poster",
- pages = "177--178",
- month = "12-16 " # jul,
- organisation = "SIGEVO",
- address = "Dublin, Ireland",
-
DOI = "
doi:10.1145/2001858.2001957",
- publisher = "ACM",
- publisher_address = "New York, NY, USA",
- abstract = "The reward functions that drive reinforcement learning systems are generally derived directly from the descriptions of the problems that the systems are being used to solve. In some problem domains, however, alternative reward functions may allow systems to learn more quickly or more effectively. Here we describe work on the use of genetic programming to find novel reward functions that improve learning system performance. We briefly present the core concepts of our approach, our motivations in developing it, and reasons to believe that the approach has promise for the production of highly successful adaptive technologies. Experimental results are presented and analysed in our full report [3].",
-
notes = "Also known as \cite{2001957} Distributed on CD-ROM at
GECCO-2011.
ACM Order Number 910112.",
