Revisiting the Arcade Learning Environment: Evaluation Protocols and Open Problems for General Agents

Machado, Marlos C., Bellemare, Marc G., Talvitie, Erik, Veness, Joel, Hausknecht, Matthew, Bowling, Michael·2018·Journal of Artificial Intelligence Research

Cited by (358)

Items 301–358 of 358

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  5. Non-Stationary Reinforcement-Learning Based Dimensionality Reduction for Multi-objective Optimization of Wetland Design
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  9. Navigating the Landscape of Multiplayer Games to Probe the Drosophila of AI
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  11. Learning from limited experience: Real-world robot navigation from a single traversal
    Jake Bruce · 2020 · Queensland University of Technology
  12. Testbeds for Reinforcement Learning.
    Banafsheh Rafiee · 2020 · arXiv (Cornell University)
  13. Escaping Stochastic Traps with Aleatoric Mapping Agents.
    Augustine N. Mavor-Parker, Kimberly A. Young, Caswell Barry, Lewis D. Griffin · 2021 · arXiv (Cornell University)
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    Changnan Xiao, Haosen Shi, Jiajun Fan, Shihong Deng · 2021 · arXiv (Cornell University)
  15. Teachability And Interpretability In Reinforcement Learning
    Jeevan Rajagopal · 2021 · Lincoln (University of Nebraska)
  16. Investigating Alternatives to the Root Mean Square for Adaptive Gradient Methods
    Brett Daley, Christopher Amato · 2021 · arXiv (Cornell University)
  17. GrowSpace: Learning How to Shape Plants
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  20. Wasserstein Distance Maximizing Intrinsic Control
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  25. Influencing Exploration in Actor-Critic Reinforcement Learning Algorithms
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  26. Improving Deep Deterministic Policy Gradient with Compact Experience Replay
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  28. Learning the Beneficial, Forgetting the Harmful: High generalization reinforcement learning with in evolving representations
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  33. Evaluating the Impact of Data Curation on Off-Policy Reinforcement Learning
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  35. A Sim-to-Real Deep Reinforcement Learning Framework for Gear Center Distance Optimization in Intelligent Machine Tool Assembly
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  38. Representations for generalization in Reinforcement Learning
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    Kaiser, Lukasz, Babaeizadeh, Mohammad, Milos, Piotr, Osinski, Blazej, Campbell, Roy H, Czechowski, Konrad, Erhan, Dumitru, Finn, Chelsea, Kozakowski, Piotr, Levine, Sergey et al. · 2019 · arXiv
  50. Meta-World: A Benchmark and Evaluation for Multi-Task and Meta Reinforcement Learning
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  51. Mastering Atari with Discrete World Models
    Hafner, Danijar, Lillicrap, Timothy, Norouzi, Mohammad, Ba, Jimmy · 2020 · arXiv
  52. Go-Explore: a New Approach for Hard-Exploration Problems
    Ecoffet, Adrien, Huizinga, Joost, Lehman, Joel, Stanley, Kenneth O., Clune, Jeff · 2019 · arXiv
  53. Leveraging Procedural Generation to Benchmark Reinforcement Learning
    Cobbe, Karl, Hesse, Christopher, Hilton, Jacob, Schulman, John · 2019 · arXiv
  54. Deep Reinforcement Learning at the Edge of the Statistical Precipice
    Agarwal, Rishabh, Schwarzer, Max, Castro, Pablo Samuel, Courville, Aaron, Bellemare, Marc G. · 2021 · arXiv
  55. Measuring the Reliability of Reinforcement Learning Algorithms
    Chan, Stephanie C. Y., Fishman, Samuel, Canny, John, Korattikara, Anoop, Guadarrama, Sergio · 2019 · arXiv
  56. Observational Overfitting in Reinforcement Learning
    Song, Xingyou, Jiang, Yiding, Tu, Stephen, Du, Yilun, Neyshabur, Behnam · 2019 · arXiv
  57. The Benchmark Lottery
    Dehghani, Mostafa, Tay, Yi, Gritsenko, Alexey A., Zhao, Zhe, Houlsby, Neil, Diaz, Fernando, Metzler, Donald, Vinyals, Oriol · 2021 · arXiv
  58. Going Beyond Linear Transformers with Recurrent Fast Weight Programmers
    Irie, Kazuki, Schlag, Imanol, Csordás, Róbert, Schmidhuber, Jürgen · 2021 · arXiv