Fetching the paper…
Reading the bibliography…
Robot sports, characterized by well-defined objectives, explicit rules, and dynamic interactions, present ideal scenarios for demonstrating embodied intelligence.
A. E. Elo and S. Sloan, “The rating of chessplayers: Past and present,” 1978
1978
Earlier work this paper cites.
H. Kitano, M. Asada, Y. Kuniyoshi, I. Noda, and E. Osawa, “Robocup: The robot world cup initiative,” in Proceedings of the first international conference on Autonomous agents
1997
Earlier work this paper cites.
S. Bouabdallah, P. Murrieri, and R. Siegwart, “Design and control of an indoor micro quadrotor,” in IEEE International Conference on Robotics and Automation, 2004. Proceedings. ICRA’04. 2004
2004
Earlier work this paper cites.
S. Behnke, M. Schreiber, J. Stuckler, R. Renner, and H. Strasdat, “See, walk, and kick: Humanoid robots start to play soccer,” in 2006 6th IEEE-RAS International Conference on Humanoid Robots
2006
Earlier work this paper cites.
A. Nakashima, Y. Ogawa, C. Liu, and Y. Hayakawa, “Robotic table tennis based on physical models of aerodynamics and rebounds,” in 2011 IEEE International Conference on Robotics and Biomimetics
2011
Earlier work this paper cites.
V. Mnih, K. Kavukcuoglu, D. Silver, A. A. Rusu, J. Veness, M. G. Bellemare, A. Graves, M. Riedmiller, A. K. Fidjeland, G. Ostrovski, et al
2015
Earlier work this paper cites.
T. Lillicrap, “Continuous control with deep reinforcement learning,” arXiv preprint arXiv:1509.02971
2015
Earlier work this paper cites.
J. Heinrich, M. Lanctot, and D. Silver, “Fictitious self-play in extensive-form games,” in International conference on machine learning
2015
Earlier work this paper cites.
F. Furrer, M. Burri, M. Achtelik, and R. Siegwart, “Rotors—a modular gazebo mav simulator framework,” Robot Operating System (ROS) The Complete Reference (Volume 1)
2016
Earlier work this paper cites.
G. Williams, N. Wagener, B. Goldfain, P. Drews, J. M. Rehg, B. Boots, and E. A. Theodorou, “Information theoretic mpc for model-based reinforcement learning,” in 2017 IEEE International Conference on Robotics and Automation (ICRA)
2017
Earlier work this paper cites.
J. Hwangbo, I. Sa, R. Siegwart, and M. Hutter, “Control of a quadrotor with reinforcement learning,” IEEE Robotics and Automation Letters
2017
Earlier work this paper cites.
2017
Earlier work this paper cites.
R. Lowe, Y. I. Wu, A. Tamar, J. Harb, O. Pieter Abbeel, and I. Mordatch, “Multi-agent actor-critic for mixed cooperative-competitive environments,” Advances in neural information processing systems
2017
Earlier work this paper cites.
M. Lanctot, V. Zambaldi, A. Gruslys, A. Lazaridou, K. Tuyls, J. Pérolat, D. Silver, and T. Graepel, “A unified game-theoretic approach to multiagent reinforcement learning,” Advances in neural information processing systems
2017
Earlier work this paper cites.
T. Osa, J. Pajarinen, G. Neumann, J. A. Bagnell, P. Abbeel, J. Peters, et al
2018
Earlier work this paper cites.
S. Fujimoto, H. Hoof, and D. Meger, “Addressing function approximation error in actor-critic methods,” in International conference on machine learning
2018
Cited alongside, same era.
T. Haarnoja, A. Zhou, P. Abbeel, and S. Levine, “Soft actor-critic: Off-policy maximum entropy deep reinforcement learning with a stochastic actor,” in International conference on machine learning
2018
Cited alongside, same era.
F. Schilling, J. Lecoeur, F. Schiano, and D. Floreano, “Learning vision-based flight in drone swarms by imitation,” IEEE Robotics and Automation Letters
2019
Cited alongside, same era.
T. Rashid, M. Samvelyan, C. S. De Witt, G. Farquhar, J. Foerster, and S. Whiteson, “Monotonic value function factorisation for deep multi-agent reinforcement learning,” Journal of Machine Learning Research
2020
Cited alongside, same era.
E. Kaufmann, L. Bauersfeld, A. Loquercio, M. Müller, V. Koltun, and D. Scaramuzza, “Champion-level drone racing using deep reinforcement learning,” Nature
2023
Later among the works it cites.
E. Kaufmann, L. Bauersfeld, A. Loquercio, M. Müller, V. Koltun, and D. Scaramuzza, “Champion-level drone racing using deep reinforcement learning,” Nature
2023
Later among the works it cites.
S. McAleer, G. Farina, G. Zhou, M. Wang, Y. Yang, and T. Sandholm, “Team-psro for learning approximate tmecor in large team games via cooperative reinforcement learning,” Advances in Neural Information Processing Systems
2023
Later among the works it cites.
2023
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
W. M. Czarnecki, G. Gidel, B. Tracey, K. Tuyls, S. Omidshafiei, D. Balduzzi, and M. Jaderberg, “Real world games look like spinning tops,” Advances in Neural Information Processing Systems
2020
Cited alongside, same era.
T. Wang and D. E. Chang, “Robust navigation for racing drones based on imitation learning and modularization,” in 2021 IEEE International Conference on Robotics and Automation (ICRA)
2021
Cited alongside, same era.
2021
Cited alongside, same era.
S. Liu, G. Lever, Z. Wang, J. Merel, S. A. Eslami, D. Hennes, W. M. Czarnecki, Y. Tassa, S. Omidshafiei, A. Abdolmaleki, et al
2022
Cited alongside, same era.
Y. Ji, Z. Li, Y. Sun, X. B. Peng, S. Levine, G. Berseth, and K. Sreenath, “Hierarchical reinforcement learning for precise soccer shooting skills using a quadrupedal robot,” 2022
2022
Cited alongside, same era.
Q. Sun, J. Fang, W. X. Zheng, and Y. Tang, “Aggressive quadrotor flight using curiosity-driven reinforcement learning,” IEEE Transactions on Industrial Electronics
2022
Cited alongside, same era.
L. Quan, L. Yin, C. Xu, and F. Gao, “Distributed swarm trajectory optimization for formation flight in dense environments,” in 2022 International Conference on Robotics and Automation (ICRA)
2022
Cited alongside, same era.
C. Yu, A. Velu, E. Vinitsky, J. Gao, Y. Wang, A. Bayen, and Y. Wu, “The surprising effectiveness of ppo in cooperative multi-agent games,” Advances in Neural Information Processing Systems
2022
Cited alongside, same era.
T. Haarnoja, B. Moran, G. Lever, S. H. Huang, D. Tirumala, J. Humplik, M. Wulfmeier, S. Tunyasuvunakool, N. Y. Siegel, R. Hafner, et al
2024
Later among the works it cites.
2024
Later among the works it cites.
2024
Later among the works it cites.
J. Chen, C. Yu, G. Li, W. Tang, X. Yang, B. Xu, H. Yang, and Y. Wang, “Multi-uav pursuit-evasion with online planning in unknown environments by deep reinforcement learning,” 2024
2024
Later among the works it cites.
Z. Luo, J. Wang, K. Liu, H. Zhang, C. Tessler, J. Wang, Y. Yuan, J. Cao, Z. Lin, F. Wang, J. Hodgins, and K. Kitani, “Smplolympics: Sports environments for physically simulated humanoids,” 2024
2024
Later among the works it cites.
B. Xu, F. Gao, C. Yu, R. Zhang, Y. Wu, and Y. Wang, “Omnidrones: An efficient and flexible platform for reinforcement learning in drone control,” IEEE Robotics and Automation Letters
2024
Later among the works it cites.
2024
Later among the works it cites.
H. Ma, J. Fan, H. Xu, and Q. Wang, “Mastering table tennis with hierarchy: a reinforcement learning approach with progressive self-play training,” Applied Intelligence
2025
Closest in time.
2025
Closest in time.
H. Sheehan, “Elopy: A python library for elo rating systems.” https://github.com/HankSheehan/EloPy , 2017 · 2025
Closest in time.