User:An anonymous user with secrets/sandbox/Error-driven learning
Error-driven learning is a type of reinforcement learning that focuses on studying agents that take actions in an environment; in order to minimize error feedback.[1]
These learning algorithms are the basis for a vast array of computational models in the brain and cognitive sciences.[1] They iteratively adjust expectations based on prediction error, simplifying the computational complexity of running simulations.[2]
Algorithms
References
- ^ a b Sadre, Ramin; Pras, Aiko (2009-06-19). Scalability of Networks and Services: Third International Conference on Autonomous Infrastructure, Management and Security, AIMS 2009 Enschede, The Netherlands, June 30 - July 2, 2009, Proceedings. Springer. ISBN 978-3-642-02627-0.
- ^ Hoppe, Dorothée B.; Hendriks, Petra; Ramscar, Michael; van Rij, Jacolien (2022-10-01). "An exploration of error-driven learning in simple two-layer networks from a discriminative learning perspective". Behavior Research Methods. 54 (5): 2221–2251. doi:10.3758/s13428-021-01711-5. ISSN 1554-3528. PMC 9579095. PMID 35032022.
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