APE: An Anti-poaching Multi-Agent Reinforcement Learning Benchmark

Prasanna Maddila, Casellas Eric, Patrick Chabrier, Régis Sabbadin, Meritxell Vinyals

Keywords: anti-poaching, multi-agent reinforcement learning, PettingZoo environment, partially observable stochastic game

Abstract: Widespread poaching threatens many endangered species today, requiring robust strategies to coordinate ranger patrols and effectively deter poachers within protected areas. Recent research has modeled this problem as a strategic game between rangers and poachers, resulting in anti-poaching becoming a popular application domain within game theory and multi-agent research communities. Unfortunately, the lack of a standard open-source implementation of the anti-poaching game hinders the reproducibility and advancement of current research in the field. This paper aims to fill this gap by providing the first open-source standardised environment for the anti-poaching game. Our contributions are as follows: (1) we formalise anti-poaching as a Partially Observable Stochastic Game; (2) we provide the Anti-Poaching Environment (APE), an open-source Python implementation of a simulator for this game using the PettingZoo API, which is compatible with many existing multi-agent reinforcement learning (MARL) libraries; and (3) we illustrate how to apply deep reinforcement-learning algorithms from the RLlib library, in order to compute cooperative and cooperative-competitive equilibria of APE instances.

Submission Number: 33