We propose a new benchmark environment for evaluating Reinforcement Learning (RL) algorithms: the PlayStation Learning Environment (PSXLE), a PlayStation emulator modified to expose a simple control API that enables rich game-state representations. We argue that the PlayStation serves as a suitable progression for agent evaluation and propose a framework for such an evaluation by building an action-driven abstraction for a game with support for the OpenAI Gym interface. Finally, we demonstrate the use of this abstraction by running OpenAI Baselines.