Symbolic Policy

Symbolic policy learning in reinforcement learning aims to create control policies represented by interpretable mathematical expressions, rather than opaque neural networks. Current research focuses on developing efficient algorithms, such as those employing genetic programming, differentiable symbolic expressions, or hybrid neuro-symbolic approaches combining neural networks with symbolic regressors, to learn these policies. This pursuit of interpretability and efficiency is crucial for deploying reinforcement learning in safety-critical applications and for gaining a deeper understanding of learned behaviors. The resulting symbolic policies offer improved transparency and potentially greater generalization capabilities compared to purely black-box methods.