# Interpretable Policies for Reinforcement Learning by Genetic Programming

arXiv: https://arxiv.org/abs/1712.04170 · HTML: https://mlsanity.com/paper/1712.04170/

## Key facts

- Cited by 48 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Dec 2017.

## Built on by (most cited first, 30 of 48)

- [A Survey on Interpretable Reinforcement Learning](https://mlsanity.com/paper/2112.13112/) (2021)
- [Symbolic Regression is NP-hard](https://mlsanity.com/paper/2207.01018/) (2022)
- [SymFormer: End-to-end symbolic regression using transformer-based architecture](https://mlsanity.com/paper/2205.15764/) (2022)
- [Overcoming Model Bias for Robust Offline Deep Reinforcement Learning](https://mlsanity.com/paper/2008.05533/) (2020)
- [Iterative Bounding MDPs: Learning Interpretable Policies via Non-Interpretable Methods](https://mlsanity.com/paper/2102.13045/) (2021)
- [A Survey of Explainable Reinforcement Learning](https://mlsanity.com/paper/2202.08434/) (2022)
- [Evolutionary Reinforcement Learning: A Survey](https://mlsanity.com/paper/2303.04150/) (2023)
- [Bridging Evolutionary Algorithms and Reinforcement Learning: A Comprehensive Survey on Hybrid Algorithms](https://mlsanity.com/paper/2401.11963/) (2024)
- [Explainable Reinforcement Learning for Broad-XAI: A Conceptual Framework and Survey](https://mlsanity.com/paper/2108.09003/) (2021)
- [ERL-Re2: Efficient Evolutionary Reinforcement Learning with Shared State Representation and Individual Policy Representation](https://mlsanity.com/paper/2210.17375/) (2022)
- [User-Interactive Offline Reinforcement Learning](https://mlsanity.com/paper/2205.10629/) (2022)
- [Efficient Symbolic Policy Learning with Differentiable Symbolic Expression](https://mlsanity.com/paper/2311.02104/) (2023)
- [Model Learning with Personalized Interpretability Estimation (ML-PIE)](https://mlsanity.com/paper/2104.06060/) (2021)
- [Comparing Model-free and Model-based Algorithms for Offline Reinforcement Learning](https://mlsanity.com/paper/2201.05433/) (2022)
- [Accountability in Offline Reinforcement Learning: Explaining Decisions with a Corpus of Examples](https://mlsanity.com/paper/2310.07747/) (2023)
- [Multimodal LLM-assisted Evolutionary Search for Programmatic Control Policies](https://mlsanity.com/paper/2508.05433/) (2025)
- [Feature-Based Interpretable Reinforcement Learning based on State-Transition Models](https://mlsanity.com/paper/2105.07099/) (2021)
- [Translate Policy to Language: Flow Matching Generated Rewards for LLM Explanations](https://mlsanity.com/paper/2502.12530/) (2025)
- [Decision Tree Induction Through LLMs via Semantically-Aware Evolution](https://mlsanity.com/paper/2503.14217/) (2025)
- [Evaluating Human-like Explanations for Robot Actions in Reinforcement Learning Scenarios](https://mlsanity.com/paper/2207.03214/) (2022)
- [Reinforcement Learning with Knowledge Representation and Reasoning: A Brief Survey](https://mlsanity.com/paper/2304.12090/) (2023)
- [Evolutionary Reinforcement Learning: A Systematic Review and Future Directions](https://mlsanity.com/paper/2402.13296/) (2024)
- [Discovering Multiple Solutions from a Single Task in Offline Reinforcement Learning](https://mlsanity.com/paper/2406.05993/) (2024)
- [Enabling Population-Level Parallelism in Tree-Based Genetic Programming for GPU Acceleration](https://mlsanity.com/paper/2501.17168/) (2025)
- [Kozax: Flexible and Scalable Genetic Programming in JAX](https://mlsanity.com/paper/2502.03047/) (2025)
- [A Survey on Explainable Reinforcement Learning: Concepts, Algorithms, Challenges](https://mlsanity.com/paper/2211.06665/) (2022)
- [S-REINFORCE: A Neuro-Symbolic Policy Gradient Approach for Interpretable Reinforcement Learning](https://mlsanity.com/paper/2305.07367/) (2023)
- [SkillTree: Explainable Skill-Based Deep Reinforcement Learning for Long-Horizon Control Tasks](https://mlsanity.com/paper/2411.12173/) (2024)
- [A Reinforcement Learning Approach to Domain-Knowledge Inclusion Using Grammar Guided Symbolic Regression](https://mlsanity.com/paper/2202.04367/) (2022)
- [Designing Novel Cognitive Diagnosis Models via Evolutionary Multi-Objective Neural Architecture Search](https://mlsanity.com/paper/2307.04429/) (2023)

## Abstract (arXiv)

The search for interpretable reinforcement learning policies is of high academic and industrial interest. Especially for industrial systems, domain experts are more likely to deploy autonomously learned controllers if they are understandable and convenient to evaluate. Basic algebraic equations are supposed to meet these requirements, as long as they are restricted to an adequate complexity. Here we introduce the genetic programming for reinforcement learning (GPRL) approach based on model-based batch reinforcement learning and genetic programming, which autonomously learns policy equations from pre-existing default state-action trajectory samples. GPRL is compared to a straight-forward method which utilizes genetic programming for symbolic regression, yielding policies imitating an existing well-performing, but non-interpretable policy. Experiments on three reinforcement learning benchmarks, i.e., mountain car, cart-pole balancing, and industrial benchmark, demonstrate the superiority of our GPRL approach compared to the symbolic regression method. GPRL is capable of producing well-performing interpretable reinforcement learning policies from pre-existing default trajectory data.