Revisiting the Arcade Learning Environment: Evaluation Protocols and Open Problems for General Agents Article Swipe
Marlos C. Machado
,
Marc G. Bellemare
,
Erik Talvitie
,
Joel Veness
,
Matthew Hausknecht
,
Michael Bowling
·
YOU?
·
· 2018
· Open Access
·
· DOI: https://doi.org/10.1613/jair.5699
YOU?
·
· 2018
· Open Access
·
· DOI: https://doi.org/10.1613/jair.5699
The Arcade Learning Environment (ALE) is an evaluation platform that poses the challenge of building AI agents with general competency across dozens of Atari 2600 games. It supports a variety of different problem settings and it has been receiving increasing attention from the scientific community, leading to some high-profile success stories such as the much publicized Deep Q-Networks (DQN). In this article we take a big picture look at how the ALE is being used by the research community. We show how diverse the evaluation methodologies in the ALE have become with time, and highlight some key concerns when evaluating agents in the ALE. We use this discussion to present some methodological best practices and provide new benchmark results using these best practices. To further the progress in the field, we introduce a new version of the ALE that supports multiple game modes and provides a form of stochasticity we call sticky actions. We conclude this big picture look by revisiting challenges posed when the ALE was introduced, summarizing the state-of-the-art in various problems and highlighting problems that remain open.
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- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.1613/jair.5699
- https://jair.org/index.php/jair/article/download/11182/26388
- OA Status
- diamond
- Cited By
- 46
- References
- 106
- Related Works
- 20
- OpenAlex ID
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All OpenAlex metadata
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https://doi.org/10.1613/jair.5699Digital Object Identifier
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Revisiting the Arcade Learning Environment: Evaluation Protocols and Open Problems for General AgentsWork title
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preprintOpenAlex work type
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enPrimary language
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2018Year of publication
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2018-03-19Full publication date if available
- Authors
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Marlos C. Machado, Marc G. Bellemare, Erik Talvitie, Joel Veness, Matthew Hausknecht, Michael BowlingList of authors in order
- Landing page
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https://doi.org/10.1613/jair.5699Publisher landing page
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https://jair.org/index.php/jair/article/download/11182/26388Direct link to full text PDF
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YesWhether a free full text is available
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diamondOpen access status per OpenAlex
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https://jair.org/index.php/jair/article/download/11182/26388Direct OA link when available
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Variety (cybernetics), Computer science, Benchmark (surveying), Key (lock), Field (mathematics), Data science, Best practice, State (computer science), Management science, Artificial intelligence, Engineering, Computer security, Political science, Mathematics, Geodesy, Geography, Pure mathematics, Law, AlgorithmTop concepts (fields/topics) attached by OpenAlex
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46Total citation count in OpenAlex
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2025: 1, 2024: 2, 2023: 8, 2022: 5, 2021: 4Per-year citation counts (last 5 years)
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20Other works algorithmically related by OpenAlex
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