# Particle Swarm Optimization for Generating Interpretable Fuzzy Reinforcement Learning Policies

arXiv: https://arxiv.org/abs/1610.05984 · HTML: https://mlsanity.com/paper/1610.05984/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Oct 2016.

## Built on by (most cited first, 19 of 19)

- [A Survey on Interpretable Reinforcement Learning](https://mlsanity.com/paper/2112.13112/) (2021)
- [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)
- [Inherently Explainable Reinforcement Learning in Natural Language](https://mlsanity.com/paper/2112.08907/) (2021)
- [TTOpt: A Maximum Volume Quantized Tensor Train-based Optimization and its Application to Reinforcement Learning](https://mlsanity.com/paper/2205.00293/) (2022)
- [Explainability in reinforcement learning: perspective and position](https://mlsanity.com/paper/2203.11547/) (2022)
- [Feature-Based Interpretable Reinforcement Learning based on State-Transition Models](https://mlsanity.com/paper/2105.07099/) (2021)
- [Behavior Constraining in Weight Space for Offline Reinforcement Learning](https://mlsanity.com/paper/2107.05479/) (2021)
- [ProtoX: Explaining a Reinforcement Learning Agent via Prototyping](https://mlsanity.com/paper/2211.03162/) (2022)
- [A Survey on Explainable Reinforcement Learning: Concepts, Algorithms, Challenges](https://mlsanity.com/paper/2211.06665/) (2022)
- [Interpretable Reinforcement Learning with Multilevel Subgoal Discovery](https://mlsanity.com/paper/2202.07414/) (2022)
- [Interactive Explanations for Reinforcement-Learning Agents](https://mlsanity.com/paper/2504.05393/) (2025)
- [Zeroth-Order Actor-Critic: An Evolutionary Framework for Sequential Decision Problems](https://mlsanity.com/paper/2201.12518/) (2022)
- [Distilling Deep RL Models Into Interpretable Neuro-Fuzzy Systems](https://mlsanity.com/paper/2209.03357/) (2022)
- [A Prompt-driven Task Planning Method for Multi-drones based on Large Language Model](https://mlsanity.com/paper/2406.00006/) (2024)
- [ASQ-IT: Interactive Explanations for Reinforcement-Learning Agents](https://mlsanity.com/paper/2301.09941/) (2023)
- [Revealing the Learning Process in Reinforcement Learning Agents Through Attention-Oriented Metrics](https://mlsanity.com/paper/2406.14324/) (2024)
- [Gradient-Based Neuroplastic Adaptation for Concurrent Optimization of Neuro-Fuzzy Networks](https://mlsanity.com/paper/2506.21771/) (2025)
- [Fuz-RL: A Fuzzy-Guided Robust Framework for Safe Reinforcement Learning under Uncertainty](https://mlsanity.com/paper/2602.20729/) (2026)

## Abstract (arXiv)

Fuzzy controllers are efficient and interpretable system controllers for continuous state and action spaces. To date, such controllers have been constructed manually or trained automatically either using expert-generated problem-specific cost functions or incorporating detailed knowledge about the optimal control strategy. Both requirements for automatic training processes are not found in most real-world reinforcement learning (RL) problems. In such applications, online learning is often prohibited for safety reasons because online learning requires exploration of the problem's dynamics during policy training. We introduce a fuzzy particle swarm reinforcement learning (FPSRL) approach that can construct fuzzy RL policies solely by training parameters on world models that simulate real system dynamics. These world models are created by employing an autonomous machine learning technique that uses previously generated transition samples of a real system. To the best of our knowledge, this approach is the first to relate self-organizing fuzzy controllers to model-based batch RL. Therefore, FPSRL is intended to solve problems in domains where online learning is prohibited, system dynamics are relatively easy to model from previously generated default policy transition samples, and it is expected that a relatively easily interpretable control policy exists. The efficiency of the proposed approach with problems from such domains is demonstrated using three standard RL benchmarks, i.e., mountain car, cart-pole balancing, and cart-pole swing-up. Our experimental results demonstrate high-performing, interpretable fuzzy policies.