Symbolic Regression Methods for Reinforcement Learning

Reinforcement learning algorithms can be used to optimally solve dynamic decision-making and control problems.With continuous-valued state and input variables, reinforcement learning algorithms must rely on function approximators to represent the value function and policy mappings.Commonly used numerical approximators, such as neural networks or basis function expansions, have two main drawbacks: they are black-box models offering no insight in the mappings learned, and they require significant trial and error tuning of their meta-parameters.In this paper, we propose a new approach to constructing smooth value functions in the form of analytic expressions by means of symbolic regression.We introduce three off-line methods for finding value functions based on a state transition model: symbolic value iteration, symbolic policy iteration, and a direct solution of the Bellman equation.The methods are illustrated on four nonlinear control problems: velocity control under friction, one-link and two-link pendulum swing-up, and magnetic manipulation.The results show that the value functions not only yield well-performing policies, but also are compact, mathematically tractable and easy to plug into other algorithms.This makes them potentially suitable for further analysis of the closed-loop system.A comparison with an alternative approach using neural networks shows that our method outperforms the neural network-based one.