Discovering Dynamic Symbolic Policies with Genetic Programming
<|reference_start|>Discovering Dynamic Symbolic Policies with Genetic Programming: Artificial intelligence techniques are increasingly being applied to solve control problems, but often rely on black-box methods without transparent output generation. To improve the interpretability and transparency in control systems, models can be defined as white-box symbolic policies described by mathematical expressions. While current approaches to learn symbolic policies focus on static policies that directly map observations to control signals, these may fail in partially observable and volatile environments. We instead consider dynamic symbolic policies with memory, optimised with genetic programming. The resulting policies are robust, and consist of easy to interpret coupled differential equations. Our results show that dynamic symbolic policies compare with black-box policies on a variety of control tasks. Furthermore, the benefit of the memory in dynamic policies is demonstrated on experiments where static policies fall short. Overall, we present a method for evolving high-performing symbolic policies that offer interpretability and transparency, which lacks in black-box models.<|reference_end|>
@article{de vries2024discovering,
    title={Discovering Dynamic Symbolic Policies with Genetic Programming},
    author={Sigur de Vries, Sander Keemink, Marcel van Gerven},
    journal={arXiv preprint arXiv:2406.02765},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.02765},
    primaryClass={cs.NE cs.LG}
}