Generalization in Reinforcement Learning: Safely Approximating the Value Function
Abstract
A straightforward approach to the curse of dimensionality in re(cid:173) inforcement learning and dynamic programming is to replace the lookup table with a generalizing function approximator such as a neu(cid:173) ral net. Although this has been successful in the domain of backgam(cid:173) mon, there is no guarantee of convergence. In this paper, we show that the combination of dynamic programming and function approx(cid:173) imation is not robust, and in even very benign cases, may produce an entirely wrong policy. We then introduce Grow-Support, a new algorithm which is safe from divergence yet can still reap the benefits of successful generalization .
Cite
Text
Boyan and Moore. "Generalization in Reinforcement Learning: Safely Approximating the Value Function." Neural Information Processing Systems, 1994.Markdown
[Boyan and Moore. "Generalization in Reinforcement Learning: Safely Approximating the Value Function." Neural Information Processing Systems, 1994.](https://mlanthology.org/neurips/1994/boyan1994neurips-generalization/)BibTeX
@inproceedings{boyan1994neurips-generalization,
title = {{Generalization in Reinforcement Learning: Safely Approximating the Value Function}},
author = {Boyan, Justin A. and Moore, Andrew W.},
booktitle = {Neural Information Processing Systems},
year = {1994},
pages = {369-376},
url = {https://mlanthology.org/neurips/1994/boyan1994neurips-generalization/}
}