Symbolic Regression Methods for Reinforcement Learning

Understand

Reinforcement learning algorithms can solve dynamic decision-making and optimal 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 little insight into the mappings learned, and they require extensive trial and error tuning of their hyper-parameters.
• In this paper, we propose a new approach to constructing smooth value functions in the form of analytic expressions by using symbolic regression.