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Multiplayer Support for the Arcade Learning Environment

arXiv 2009.09341 ↗First posted 20 Sep 2020cs.LG cs.AIcited by 8

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Abstract (arXiv)

The Arcade Learning Environment ("ALE") is a widely used library in the reinforcement learning community that allows easy programmatic interfacing with Atari 2600 games, via the Stella emulator. We introduce a publicly available extension to the ALE that extends its support to multiplayer games and game modes. This interface is additionally integrated with PettingZoo to allow for a simple Gym-like interface in Python to interact with these games. We additionally introduce experimental baselines for all environments included.

Key facts. Cited by 8 papers in the map (data through 7 Oct 2026). First posted on arXiv 20 Sep 2020.

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Citing papers per year: 2021: 3; 2022: 0; 2023: 0; 2024: 2; 2025: 3.

8 papers.

  1. 2024Gymnasium: A Standard Interface for Reinforcement Learning Environmentscited by 35
  2. 2021A Game-Theoretic Approach to Multi-Agent Trust Region Optimizationcited by 7
  3. 2021Investigation of Independent Reinforcement Learning Algorithms in Multi-Agent Environmentscited by 5
  4. 2024CALE: Continuous Arcade Learning Environmentcited by 5
  5. 2025An Extended Benchmarking of Multi-Agent Reinforcement Learning Algorithms in Complex Fully Cooperative Taskscited by 5
  6. 2025The Formalism-Implementation Gap in Reinforcement Learning Researchcited by 3
  7. 2021A coevolutionary approach to deep multi-agent reinforcement learningcited by 2
  8. 2025Moving Out: Physically-grounded Human-AI Collaborationcited by 0