Reinforcement Learning (RL) agents can learn to control a nonlinear system without using a model of the system. However, having a model brings benefits, mainly in terms of a reduced number of unsuccessful trials before achieving acceptable control performance. Several modelling approaches have been used in the RL domain, such as neural networks, local linear regression, or Gaussian processes. In this article, we focus on a technique that has not been used much so far:\ symbolic regression, based on genetic programming.

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[1] Martin Brablc, "Benchmarking Symbolic Regression and Local Linear Modelling Methods for Reinforcement Learning", IEEE Dataport, 2019. [Online]. Available: http://dx.doi.org/10.21227/5v5e-jg39. Accessed: Feb. 17, 2025.
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doi = {10.21227/5v5e-jg39},
url = {http://dx.doi.org/10.21227/5v5e-jg39},
author = {Martin Brablc },
publisher = {IEEE Dataport},
title = {Benchmarking Symbolic Regression and Local Linear Modelling Methods for Reinforcement Learning},
year = {2019} }
TY - DATA
T1 - Benchmarking Symbolic Regression and Local Linear Modelling Methods for Reinforcement Learning
AU - Martin Brablc
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PB - IEEE Dataport
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Martin Brablc. (2019). Benchmarking Symbolic Regression and Local Linear Modelling Methods for Reinforcement Learning. IEEE Dataport. http://dx.doi.org/10.21227/5v5e-jg39
Martin Brablc, 2019. Benchmarking Symbolic Regression and Local Linear Modelling Methods for Reinforcement Learning. Available at: http://dx.doi.org/10.21227/5v5e-jg39.
Martin Brablc. (2019). "Benchmarking Symbolic Regression and Local Linear Modelling Methods for Reinforcement Learning." Web.
1. Martin Brablc. Benchmarking Symbolic Regression and Local Linear Modelling Methods for Reinforcement Learning [Internet]. IEEE Dataport; 2019. Available from : http://dx.doi.org/10.21227/5v5e-jg39
Martin Brablc. "Benchmarking Symbolic Regression and Local Linear Modelling Methods for Reinforcement Learning." doi: 10.21227/5v5e-jg39