The PlayStation Reinforcement Learning Environment (PSXLE)

Carlos Purves; Cătălina Cangea; Petar Veličković

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arXiv · 1912.06101

The PlayStation Reinforcement Learning Environment (PSXLE)

Carlos Purves·Cătălina Cangea·Petar Veličković

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Abstract

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.

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Carlos Purves, Cătălina Cangea, Petar Veličković. 2019-12-12. The PlayStation Reinforcement Learning Environment (PSXLE). https://arxiv.org/abs/1912.06101

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