S-REINFORCE: A Neuro-Symbolic Policy Gradient Approach for Interpretable
  Reinforcement Learning
Rajdeep Dutta Qincheng Wang Ankur Singh Dhruv Kumarjiguda Li Xiaoli Senthilnath Jayavelu
This paper presents a novel RL algorithm, S-REINFORCE, which is designed to
generate interpretable policies for dynamic decision-making tasks. The proposed
algorithm leverages two types of function approximators, namely Neural Network
(NN) and Symbolic Regressor (SR), to produce numerical and symbolic policies,
respectively. The NN component learns to generate a numerical probability
distribution over the possible actions using a policy gradient, while the SR
component captures the functional form that relates the associated states with
the action probabilities. The SR-generated policy expressions are then utilized
through importance sampling to improve the rewards received during the learning
process. We have tested the proposed S-REINFORCE algorithm on various dynamic
decision-making problems with low and high dimensional action spaces, and the
results demonstrate its effectiveness and impact in achieving interpretable
solutions. By leveraging the strengths of both NN and SR, S-REINFORCE produces
policies that are not only well-performing but also easy to interpret, making
it an ideal choice for real-world applications where transparency and causality
are crucial.