Mastering Atari, Go, chess and shogi by planning with a learned model

Citations

Cited by:

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2. Saipraneeth Devunuri & Shirin Qiam & Lewis J. Lehe, 2024. "ChatGPT for GTFS: benchmarking LLMs on GTFS semantics... and retrieval," Public Transport, Springer, vol. 16(2), pages 333-357, June.
3. Guangyuan Li & Baobao Song & Harinder Singh & V. B. Surya Prasath & H. Leighton Grimes & Nathan Salomonis, 2023. "Decision level integration of unimodal and multimodal single cell data with scTriangulate," Nature Communications, Nature, vol. 14(1), pages 1-16, December.
4. Weiwu Ren & Jialin Zhu & Hui Qi & Ligang Cong & Xiaoqiang Di, 2022. "Dynamic optimization of intersatellite link assignment based on reinforcement learning," International Journal of Distributed Sensor Networks, , vol. 18(2), pages 15501477211, February.
5. Christopher R. Madan, 2020. "Considerations for Comparing Video Game AI Agents with Humans," Challenges, MDPI, vol. 11(2), pages 1-12, August.
6. Tasos Papagiannis & Georgios Alexandridis & Andreas Stafylopatis, 2022. "Pruning Stochastic Game Trees Using Neural Networks for Reduced Action Space Approximation," Mathematics, MDPI, vol. 10(9), pages 1-16, May.
7. Huang, Ruchen & He, Hongwen & Gao, Miaojue, 2023. "Training-efficient and cost-optimal energy management for fuel cell hybrid electric bus based on a novel distributed deep reinforcement learning framework," Applied Energy, Elsevier, vol. 346(C).
8. Jorge Ramírez-Ruiz & Dmytro Grytskyy & Chiara Mastrogiuseppe & Yamen Habib & Rubén Moreno-Bote, 2024. "Complex behavior from intrinsic motivation to occupy future action-state path space," Nature Communications, Nature, vol. 15(1), pages 1-15, December.
9. Nejat Anbarci & Mehmet S Ismail, 2024. "AI-powered mechanisms as judges: Breaking ties in chess," PLOS ONE, Public Library of Science, vol. 19(11), pages 1-17, November.
   • Nejat Anbarci & Mehmet S. Ismail, 2022. "AI-powered mechanisms as judges: Breaking ties in chess," Papers 2210.08289, arXiv.org, revised Jul 2024.
10. Gokhale, Gargya & Claessens, Bert & Develder, Chris, 2022. "Physics informed neural networks for control oriented thermal modeling of buildings," Applied Energy, Elsevier, vol. 314(C).
11. Syed Ghazi Sarwat & Timoleon Moraitis & C. David Wright & Harish Bhaskaran, 2022. "Chalcogenide optomemristors for multi-factor neuromorphic computation," Nature Communications, Nature, vol. 13(1), pages 1-9, December.
12. Marcel Rolf Pfeifer, 2021. "Development of a Smart Manufacturing Execution System Architecture for SMEs: A Czech Case Study," Sustainability, MDPI, vol. 13(18), pages 1-23, September.
13. Christoph Graf & Viktor Zobernig & Johannes Schmidt & Claude Klöckl, 2024. "Computational Performance of Deep Reinforcement Learning to Find Nash Equilibria," Computational Economics, Springer;Society for Computational Economics, vol. 63(2), pages 529-576, February.
14. Boute, Robert N. & Gijsbrechts, Joren & van Jaarsveld, Willem & Vanvuchelen, Nathalie, 2022. "Deep reinforcement learning for inventory control: A roadmap," European Journal of Operational Research, Elsevier, vol. 298(2), pages 401-412.
15. Zhenchong Mo & Lin Gong & Mingren Zhu & Junde Lan, 2024. "The Generative Generic-Field Design Method Based on Design Cognition and Knowledge Reasoning," Sustainability, MDPI, vol. 16(22), pages 1-34, November.
16. Li, Wenqing & Ni, Shaoquan, 2022. "Train timetabling with the general learning environment and multi-agent deep reinforcement learning," Transportation Research Part B: Methodological, Elsevier, vol. 157(C), pages 230-251.
17. Pavirani, Fabio & Van Gompel, Jonas & Karimi Madahi, Seyed Soroush & Claessens, Bert & Develder, Chris, 2025. "Predicting and publishing accurate imbalance prices using Monte Carlo Tree Search," Applied Energy, Elsevier, vol. 392(C).
18. Tang, Tao & Chai, Simin & Wu, Wei & Yin, Jiateng & D’Ariano, Andrea, 2025. "A multi-task deep reinforcement learning approach to real-time railway train rescheduling," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 194(C).
19. Jin Li & Ye Luo & Zigan Wang & Xiaowei Zhang, 2021. "Asymptotic Theory for IV-Based Reinforcement Learning with Potential Endogeneity," Papers 2103.04021, arXiv.org, revised Dec 2024.
20. Jinke Yao & Jiachen Xu & Ning Zhang & Yajuan Guan, 2023. "Model-Based Reinforcement Learning Method for Microgrid Optimization Scheduling," Sustainability, MDPI, vol. 15(12), pages 1-18, June.
21. He, Hongwen & Su, Qicong & Huang, Ruchen & Niu, Zegong, 2024. "Enabling intelligent transferable energy management of series hybrid electric tracked vehicle across motion dimensions via soft actor-critic algorithm," Energy, Elsevier, vol. 294(C).
22. Spyridon Samothrakis, 2021. "Artificial Intelligence inspired methods for the allocation of common goods and services," PLOS ONE, Public Library of Science, vol. 16(9), pages 1-16, September.
23. Alexandros A. Lavdas & Nikos A. Salingaros, 2021. "Can Suboptimal Visual Environments Negatively Affect Children’s Cognitive Development?," Challenges, MDPI, vol. 12(2), pages 1-12, November.
24. Rishi Rajalingham & Aída Piccato & Mehrdad Jazayeri, 2022. "Recurrent neural networks with explicit representation of dynamic latent variables can mimic behavioral patterns in a physical inference task," Nature Communications, Nature, vol. 13(1), pages 1-15, December.
25. Christoph Graf & Viktor Zobernig & Johannes Schmidt & Claude Klockl, 2021. "Computational Performance of Deep Reinforcement Learning to find Nash Equilibria," Papers 2104.12895, arXiv.org.
26. Bálint Kővári & Lászlo Szőke & Tamás Bécsi & Szilárd Aradi & Péter Gáspár, 2021. "Traffic Signal Control via Reinforcement Learning for Reducing Global Vehicle Emission," Sustainability, MDPI, vol. 13(20), pages 1-18, October.