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
- Reinforcement Learning and Dynamical SystemsBjörn Lindenberg · 2023
- Novelty Detection in Reinforcement Learning with World ModelsZollicoffer, Geigh, Eaton, Kenneth, Balloch, Jonathan, Kim, Julia, Zhou, Wei, Wright, Robert, Riedl, Mark O. · 2023 · arXiv
- Conjugated Discrete Distributions for Distributional Reinforcement LearningBjörn Lindenberg, Jonas Nordqvist, Karl‐Olof Lindahl · 2022 · Proceedings of the AAAI Conference on Artificial Intelligence
- The Text-Based Adventure AI CompetitionTimothy J. Atkinson, Hendrik Baier, Tara Copplestone, Sam Devlin, Jerry Swan · 2018 · arXiv (Cornell University)
- Non-Stationary Reinforcement-Learning Based Dimensionality Reduction for Multi-objective Optimization of Wetland DesignAndrew Hoblitzell, Meghna Babbar‐Sebens, Snehasis Mukhopadhyay · 2019
- Self-Supervised Object-Level Deep Reinforcement Learning.William S. Agnew, Pedro Domingos · 2020 · arXiv (Cornell University)
- Understanding Finite-State Representations of Recurrent Policy NetworksMohamad H. Danesh, Anurag Koul, Alan Fern, Saeed Khorram · 2020 · arXiv (Cornell University)
- Exploration by Maximizing Rényi Entropy for Zero-Shot Meta RL.Chuheng Zhang, Yuanying Cai, Longbo Huang, Jian Li · 2020 · arXiv (Cornell University)
- Navigating the Landscape of Multiplayer Games to Probe the Drosophila of AIShayegan Omidshafiei, Karl Tuyls, Wojciech Marian Czarnecki, Francisco C. Santos, Mark Rowland, Jerome T. Connor, Daniel Hennes, Paul Friedrich Müller, Julien Pérolat, Bart De Vylder et al. · 2020 · arXiv (Cornell University)
- Relevance-Guided Modeling of Object Dynamics for Reinforcement LearningWilliam S. Agnew, Pedro Domingos · 2020 · arXiv (Cornell University)
- Learning from limited experience: Real-world robot navigation from a single traversalJake Bruce · 2020 · Queensland University of Technology
- Testbeds for Reinforcement Learning.Banafsheh Rafiee · 2020 · arXiv (Cornell University)
- Escaping Stochastic Traps with Aleatoric Mapping Agents.Augustine N. Mavor-Parker, Kimberly A. Young, Caswell Barry, Lewis D. Griffin · 2021 · arXiv (Cornell University)
- CASA: A Bridge Between Gradient of Policy Improvement and Policy Evaluation.Changnan Xiao, Haosen Shi, Jiajun Fan, Shihong Deng · 2021 · arXiv (Cornell University)
- Teachability And Interpretability In Reinforcement LearningJeevan Rajagopal · 2021 · Lincoln (University of Nebraska)
- Investigating Alternatives to the Root Mean Square for Adaptive Gradient MethodsBrett Daley, Christopher Amato · 2021 · arXiv (Cornell University)
- GrowSpace: Learning How to Shape PlantsYasmeen Hitti, Ionelia Buzatu, Manuel Del Verme, Mark G. Lefsrud, Florian Golemo, Audrey Durand · 2021 · arXiv (Cornell University)
- An internal supplemental action measurement to increase the gap of action values and reduce the sensitivity to overestimation errorHaolin Wu, Hui Li, Jianwei Zhang, Zhuang Wang, Zhiyong Huang · 2021 · Journal of Experimental & Theoretical Artificial Intelligence
- An Oracle and Observations for the OpenAI Gym / ALE Freeway EnvironmentJames S. Plank, Catherine D. Schuman, Robert M. Patton · 2021 · arXiv (Cornell University)
- Wasserstein Distance Maximizing Intrinsic ControlIshan Durugkar, Steven Hansen, Stephen Spencer, Volodymyr Mnih · 2021 · arXiv (Cornell University)
- Experimental Evaluation of Deep Reinforcement Learning AlgorithmsNikola Mrzljak, Tomislav Hrkać · 2019 · Proceedings of the Croatian Computer Vision Workshop
- Adversarial Discriminative Feature Separation for Generalization in Reinforcement LearningYong kang Liu, Chunwei Wu, Xidong Xi, Yan Li, Guitao Cao, Wenming Cao, Hong Wang · 2022 · International Joint Conference on Neural Networks (IJCNN)
- References2023 · The MIT Press eBooks
- Investigating the Practicality of Existing Reinforcement Learning Algorithms: A Performance ComparisonOlivia P. Dizon-Paradis, Stephen E. Wormald, Daniel E. Capecci, Avanti Bhandarkar, Damon L. Woodard · 2023
- Influencing Exploration in Actor-Critic Reinforcement Learning AlgorithmsAndrew R Gough · 2018
- Improving Deep Deterministic Policy Gradient with Compact Experience ReplayDaniel Rodrigues Rezende Neves, Lucila Ishitani, Zenilton K. G. Patrocínio · 2024 · Research Square
- An Efficient Multi-Agent Policy Self-Play Learning Method Aiming at Seize-Control ScenariosHuaqing Zhang, Hongbin Ma, Xiaofei Zhang, Wang Li, Minglei Han, Hui Chen, Ao Ding · 2024 · Unmanned Systems
- Learning the Beneficial, Forgetting the Harmful: High generalization reinforcement learning with in evolving representationsJiawei Zheng, Yonghong Song, Guojiao Lin, Jiayi Duan, Hao Lin, Shuaitao Li · 2024 · Neurocomputing
- The Game Boy Learning EnvironmentEdoardo Fazzari, Donato Romano, Fabrizio Falchi, Cesare Stefanini · 2025 · IEEE Transactions on Games
- On the Mistaken Assumption of Interchangeable Deep Reinforcement Learning ImplementationsRajdeep Singh Hundal, Yan Xiao, Xiaochun Cao, Jin Song Dong, Manuel Rigger · 2025 · ACM/IEEE International Conference on Software Engineering: Companion Proceedings (ICSE-Companion)
- Mastering Visual Reinforcement Learning via Positive Unlabeled Policy-Guided ContrastZehua Zang, Qirui Ji, Rui Wang, Kai Li, Lixiang Liu, Fuchun Sun · 2025 · Lecture notes in computer science
- Time-Efficient Uncertainty Estimation Based on Target Networks in Deep Reinforcement LearningXionglue Li, Yongguang Wang, Lijuan Sun, Yongyong Chen · 2025 · Proceedings - International Conference on Image Processing
- Evaluating the Impact of Data Curation on Off-Policy Reinforcement LearningMatthias Blum, Shahram Eivazi · 2025 · Lecture notes in computer science
- Optimal Control of Unknown Nonlinear Systems via a Multimodel-Based Multistep Reinforcement Learning FrameworkShanwu Li, Yongchao Yang · 2025 · IEEE Transactions on Artificial Intelligence
- A Sim-to-Real Deep Reinforcement Learning Framework for Gear Center Distance Optimization in Intelligent Machine Tool AssemblyChih‐Lin Lin, Wei-Kai Jhao, Chih‐En Kuo · 2026 · SSRN Electronic Journal
- Optimizing Deep Reinforcement Learning for Atari Games: A Study on Preprocessing Techniques and Neural Network ArchitectureDavid David, Agung Trisetyarso, Lukas, Widodo Budiharto · 2025 · International Conference on Information and Communications Technology (ICOIACT)
- Intrinsically Motivated and Interactive Reinforcement Learning : a Developmental ApproachPierre Fournier · 2019
- Representations for generalization in Reinforcement LearningDavid Bertoin · 2023
- Distillation de réseaux de neurones pour la généralisation et le transfert en apprentissage par renforcementValentin Guillet · 2022
- Mamba World Model for RL: Enhancing Long-term Memory in Model-based Reinforcement LearningMingcong Cao, Yucheng Huang, Suat Gümüşsoy, Umit Yusuf Ogras · 2025 · International Conference on Machine Learning and Applications
- Automatic Symbolic Goal Abstraction via Reachability Analysis in Hierarchical Reinforcement LearningMehdi Zadem · 2024
- Sampling-Based Search Algorithms in GamesJérôme Arjonilla · 2024 · theses.fr (ABES)
- A Surrogate-Assisted Deep Reinforcement Learning Framework for Gear Center Distance Optimization in Machine Tool AssemblyChih‐Lin Lin, Wei-Kai Jhao, Chih‐En Kuo · 2026 · The International Journal of Advanced Manufacturing Technology
- System 1, System 2: Fusing Asynchronous Vision-Language Guidance with Fast Continuous Embodied Agent ControlHussam Barham, Mohammad Fasha, Mohammad Ali Al Khaldy · 2026 · International Conference on Computational Intelligence Approaches and Applications (ICCIAA)
- Interpretable Control with Graph-Based Genetic ProgrammingGiorgia Nadizar · 2026 · Genetic and evolutionary computation
- A Research Agenda for Usability and Generalisation in Reinforcement LearningDennis J. N. J. Soemers, Spyridon Samothrakis, K. Driessens, M. Winands · 2024 · International Conference on Agents and Artificial Intelligence
- Balancing Diversity and Focus in Exploration with Dynamic State ClusteringTim Joseph, Marcus Fechner, Philipp Stegmaier, Karam Daaboul, J. Marius Zöllner · 2026 · Lecture notes in computer science
- Dream to Control: Learning Behaviors by Latent ImaginationHafner, Danijar, Lillicrap, Timothy, Ba, Jimmy, Norouzi, Mohammad · 2019 · arXiv
- Model-Based Reinforcement Learning for AtariKaiser, Lukasz, Babaeizadeh, Mohammad, Milos, Piotr, Osinski, Blazej, Campbell, Roy H, Czechowski, Konrad, Erhan, Dumitru, Finn, Chelsea, Kozakowski, Piotr, Levine, Sergey et al. · 2019 · arXiv
- Meta-World: A Benchmark and Evaluation for Multi-Task and Meta Reinforcement LearningYu, Tianhe, Quillen, Deirdre, He, Zhanpeng, Julian, Ryan, Narayan, Avnish, Shively, Hayden, Bellathur, Adithya, Hausman, Karol, Finn, Chelsea, Levine, Sergey · 2019 · arXiv
- Mastering Atari with Discrete World ModelsHafner, Danijar, Lillicrap, Timothy, Norouzi, Mohammad, Ba, Jimmy · 2020 · arXiv
- Go-Explore: a New Approach for Hard-Exploration ProblemsEcoffet, Adrien, Huizinga, Joost, Lehman, Joel, Stanley, Kenneth O., Clune, Jeff · 2019 · arXiv
- Leveraging Procedural Generation to Benchmark Reinforcement LearningCobbe, Karl, Hesse, Christopher, Hilton, Jacob, Schulman, John · 2019 · arXiv
- Deep Reinforcement Learning at the Edge of the Statistical PrecipiceAgarwal, Rishabh, Schwarzer, Max, Castro, Pablo Samuel, Courville, Aaron, Bellemare, Marc G. · 2021 · arXiv
- Measuring the Reliability of Reinforcement Learning AlgorithmsChan, Stephanie C. Y., Fishman, Samuel, Canny, John, Korattikara, Anoop, Guadarrama, Sergio · 2019 · arXiv
- Observational Overfitting in Reinforcement LearningSong, Xingyou, Jiang, Yiding, Tu, Stephen, Du, Yilun, Neyshabur, Behnam · 2019 · arXiv
- The Benchmark LotteryDehghani, Mostafa, Tay, Yi, Gritsenko, Alexey A., Zhao, Zhe, Houlsby, Neil, Diaz, Fernando, Metzler, Donald, Vinyals, Oriol · 2021 · arXiv
- Going Beyond Linear Transformers with Recurrent Fast Weight ProgrammersIrie, Kazuki, Schlag, Imanol, Csordás, Róbert, Schmidhuber, Jürgen · 2021 · arXiv