# A Three-Dimensional Pursuit-Evasion Game Based on Fuzzy Actor-Critic Learning Algorithm

arXiv: https://arxiv.org/abs/2503.08013 · HTML: https://mlsanity.com/paper/2503.08013/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in RL for robot navigation and UAV control, one of 15 directions in Deep reinforcement learning methods.
- Builds on 4 of its 16 references that are in the map.
- First posted on arXiv 11 Mar 2025.

## Placed in

- Deep reinforcement learning methods / [RL for robot navigation and UAV control](https://mlsanity.com/deeprl/rl-for-robot-navigation-and-uav-control/)

## Builds on (most cited first, 4 of 4)

- [An Introduction to Pursuit-evasion Differential Games](https://mlsanity.com/paper/2003.05013/) (2020)
- [Multiple Pursuer Multiple Evader Differential Games](https://mlsanity.com/paper/1911.03806/) (2019)
- [Defending a Perimeter from a Ground Intruder Using an Aerial Defender: Theory and Practice](https://mlsanity.com/paper/2109.02852/) (2021)
- [The Pursuit and Evasion of Drones Attacking an Automated Turret](https://mlsanity.com/paper/2107.12660/) (2021)

## Abstract (arXiv)

Most of the existing research on pursuit-evasion game (PEG) is conducted in a two-dimensional (2D) environment. In this paper, we investigate the PEG in a 3D space. We extend the Apollonius circle (AC) to the 3D space and introduce its detailed analytical form. To enhance the capture efficiency, we derive the optimal motion space for both the pursuer and the evader. To address the issue arising from a discrete state space, we design a fuzzy actor-critic learning (FACL) algorithm to obtain the agents' strategies. To improve learning performance, we devise a reward function for the agents, which enables obstacle avoidance functionality. The effectiveness of the proposed algorithm is validated through simulation experiments.