APE: An Anti-poaching Multi-Agent Reinforcement Learning Benchmark
Résumé
Widespread poaching threatens many endangered species today, requiring robust strategies to coordinate ranger patrols and effectively deter poachers within protected areas. Recent research has modelled 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. Our project is published at https://forgemia.inra.fr/chip-gt/antipoaching.