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.