S-REINFORCE: A Neuro-symbolic Policy Gradient Approach For Interpretable Reinforcement Learning

Abstract

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-REINF

Stats

• citations0
• S2 citations—
• github stars0
• HF likes0
• heat score0.00
• arxiv keydutta2023s

Related papers

• Improving Policy Gradient By Exploring Under-appreciated Rewards (2016)0.00
• Three Pathways To Neurosymbolic Reinforcement Learning With Interpretable Model And Policy Networks (2024)0.00
• "so, Tell Me About Your Policy...": Distillation Of Interpretable Policies From Deep Reinforcement Learning Agents (2025)0.00
• Mitigating Information Loss In Tree-based Reinforcement Learning Via Direct Optimization (2024)0.00
• Generation Of Policy-level Explanations For Reinforcement Learning (2019)11.39
• Social Interpretable Reinforcement Learning (2024)3.58
• End-to-end Neuro-symbolic Reinforcement Learning With Textual Explanations (2024)0.00
• Interpretable End-to-end Neurosymbolic Reinforcement Learning Agents (2024)0.00