interpretable policy

In Search of Trees: Decision-Tree Policy Synthesis for Black-Box Systems via Search AAAI 2025 LICORICE: Label-Efficient Concept-Based Interpretable Reinforcement Learning ICLR 2025 SMoSE: Sparse Mixture of Shallow Experts for Interpretable Reinforcement Learning in Continuous Control Tasks AAAI 2025 Piecewise Linear Parametrization of Policies: Towards Interpretable Deep Reinforcement Learning ICLR 2024 Efficient Symbolic Policy Learning with Differentiable Symbolic Expression NeurIPS 2023 Interpretable and Explainable Logical Policies via Neurally Guided Symbolic Abstraction NeurIPS 2023 Learning Intuitive Policies Using Action Features ICML 2023 Optimal Decision Tree Policies for Markov Decision Processes IJCAI 2023 GALOIS: Boosting Deep Reinforcement Learning via Generalizable Logic Synthesis NeurIPS 2022 Interpretable Off-Policy Learning via Hyperbox Search ICML 2022 Learning Interpretable, High-Performing Policies for Autonomous Driving RSS 2022 Iterative Bounding MDPs: Learning Interpretable Policies via Non-Interpretable Methods AAAI 2021 K-N-MOMDPs: Towards Interpretable Solutions for Adaptive Management AAAI 2021 Learning to Synthesize Programs as Interpretable and Generalizable Policies NeurIPS 2021 Neuro-Symbolic Approaches for Text-Based Policy Learning EMNLP 2021 Neuro-Symbolic Reinforcement Learning with First-Order Logic EMNLP 2021 Interpretable and Personalized Apprenticeship Scheduling: Learning Interpretable Scheduling Policies from Heterogeneous User Demonstrations NeurIPS 2020 Verifiable and Interpretable Reinforcement Learning through Program Synthesis AAAI 2019 Programmatically Interpretable Reinforcement Learning ICML 2018