The PlayStation Reinforcement Learning Environment (PSXLE)
This provides a domain-specific benchmark for RL researchers, but it is incremental as it adapts existing emulator technology to a new interface.
The authors introduced the PlayStation Reinforcement Learning Environment (PSXLE), a new benchmark using a PlayStation emulator with a control API for evaluating RL algorithms, and demonstrated its application by running OpenAI Baselines.
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.