In today’s world, endangered species are threatened by widespread poaching, requiring intel- ligent land patrol strategies to effectively detect and prevent such activities. Several recent works have developed game-theoretic models for anti-poaching, wherein determining equilib- rium strategies, often based on the Nash Equilibrium (NE) 1 , leads to effective patrol strate- gies. Additionally, due to the complexity and imperfect knowledge of the models, Multi- Agent Reinforcement Learning (MARL) methods are usually proposed to learn these strategies. Yet, even with anti-poaching emerging as a popular domain for MARL, the absence of both a general model and a publicly accessible implementation has hindered both the evaluation and development of new solutions. In this context, the objective of this work is two-fold: (i) formalize anti-poaching as a Partially Observable Stochastic Game (POSG) capable of gen- eralizing existing models; and (ii) provide a publicly available implementation of this POSG in PettingZoo (one of the most popular APIs to implement MARL environments).