Corpus ID: 54457643

ToyBox: Better Atari Environments for Testing Reinforcement Learning Agents

@article{Foley2018ToyBoxBA,
  title={ToyBox: Better Atari Environments for Testing Reinforcement Learning Agents},
  author={John Foley and Emma Tosch and Kaleigh Clary and David D. Jensen},
  journal={ArXiv},
  year={2018},
  volume={abs/1812.02850}
}
  • John Foley, Emma Tosch, +1 author David D. Jensen
  • Published 2018
  • Computer Science
  • ArXiv
  • It is a widely accepted principle that software without tests has bugs. Testing reinforcement learning agents is especially difficult because of the stochastic nature of both agents and environments, the complexity of state-of-the-art models, and the sequential nature of their predictions. Recently, the Arcade Learning Environment (ALE) has become one of the most widely used benchmark suites for deep learning research, and state-of-the-art Reinforcement Learning (RL) agents have been shown to… CONTINUE READING

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