Dutta, R., Wang, Q., Singh, A., Kumarjiguda, D., Xiaoli, L., & Jayavelu, S. (2024, April 14). Interpretable Policy Extraction with Neuro-Symbolic Reinforcement Learning. ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). https://doi.org/10.1109/icassp48485.2024.10446037
Abstract:
This paper presents a novel RL algorithm, S-REINFORCE, designed by leveraging two types of function approximators, namely Neural Network (NN) and Symbolic Regressor (SR), to produce numerical and symbolic policies for dynamic decision-making tasks, respectively. A symbolic policy uncovers functional relations between the underlying states and action-probabilities. Further, the symbolic policy is utilized through importance sampling (IS) to improve the rewards received during the learning process. The effectiveness of S-REINFORCE has been validated on various dynamic decision-making problems involving low and high dimensional action spaces. The results obtained clearly demonstrate that by leveraging the complementary strengths of NN and SR, S-REINFORCE generates policies that exhibit both good performance and interpretability. This makes S-REINFORCE an excellent choice for real-world applications where transparency and causality play a crucial role.
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Publisher Copyright
Funding Info:
This research / project is supported by the A*STAR - AME Programmatic Fund
Grant Reference no. : A1898b0043