  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. We build
an action-driven abstraction for a PlayStation game with support for the OpenAI
Gym interface and demonstrate its use by running OpenAI Baselines.
