# Fuzzy Logic Theory-based Adaptive Reward Shaping for Robust Reinforcement Learning (FARS)

arXiv: https://arxiv.org/abs/2604.15772 · HTML: https://mlsanity.com/paper/2604.15772/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Deep RL algorithms and representations, one of 15 directions in Deep reinforcement learning methods.
- Builds on 9 of its 29 references that are in the map.
- First posted on arXiv 17 Apr 2026.

## Placed in

- Deep reinforcement learning methods / [Deep RL algorithms and representations](https://mlsanity.com/deeprl/deep-rl-algorithms-and-representations/)

## Builds on (most cited first, 9 of 9)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Differential Flatness of Quadrotor Dynamics Subject to Rotor Drag for Accurate Tracking of High-Speed Trajectories](https://mlsanity.com/paper/1712.02402/) (2017)
- [PencilNet: Zero-Shot Sim-to-Real Transfer Learning for Robust Gate Perception in Autonomous Drone Racing](https://mlsanity.com/paper/2207.14131/) (2022)
- [Event-based Navigation for Autonomous Drone Racing with Sparse Gated Recurrent Network](https://mlsanity.com/paper/2204.02120/) (2022)
- [Environment as Policy: Learning to Race in Unseen Tracks](https://mlsanity.com/paper/2410.22308/) (2024)
- [Mastering Diverse, Unknown, and Cluttered Tracks for Robust Vision-Based Drone Racing](https://mlsanity.com/paper/2512.09571/) (2025)
- [Image Generation for Efficient Neural Network Training in Autonomous Drone Racing](https://mlsanity.com/paper/2008.02596/) (2020)
- [Contact Energy Based Hindsight Experience Prioritization](https://mlsanity.com/paper/2312.02677/) (2023)
- [Continual Learning for Robust Gate Detection under Dynamic Lighting in Autonomous Drone Racing](https://mlsanity.com/paper/2405.01054/) (2024)

## Abstract (arXiv)

Reinforcement learning (RL) often struggles in real-world tasks with high-dimensional state spaces and long horizons, where sparse or fixed rewards severely slow down exploration and cause agents to get trapped in local optima. This paper presents a fuzzy logic based reward shaping method that integrates human intuition into RL reward design. By encoding expert knowledge into adaptive and interpreable terms, fuzzy rules promote stable learning and reduce sensitivity to hyperparameters. The proposed method leverages these properties to adapt reward contributions based on the agent state, enabling smoother transitions between fast motion and precise control in challenging navigation tasks. Extensive simulation results on autonomous drone racing benchmarks show stable learning behavior and consistent task performance across scenarios of increasing difficulty. The proposed method achieves faster convergence and reduced performance variability across training seeds in more challenging environments, with success rates improving by up to approximately 5 percent compared to non fuzzy reward formulations.