Ilge Akkaya, Marcin Andrychowicz, Maciek Chociej, Mateusz Litwin, Bob McGrew, Arthur Petron, Alex Paino, Matthias Plappert, Glenn Powell, Raphael Ribas, et al. Solving rubik's cube with a robot hand. arXiv preprint arXiv:1910.07113, 2019.
Gabriel Barth-Maron, Matthew W Hoffman, David Budden, Will Dabney, Dan Horgan, Dhruva Tb, Alistair Muldal, Nicolas Heess, and Timothy Lillicrap. Distributed distributional deterministic policy gradients. arXiv preprint arXiv:1804.08617, 2018.
Dimitri Bertsekas. Dynamic programming and optimal control: Volume I, volume 1. Athena scientific, 2012.
Greg Brockman, Vicki Cheung, Ludwig Pettersson, Jonas Schneider, John Schulman, Jie Tang, and Wojciech Zaremba. Openai gym. CoRR, abs/1606.01540, 2016. URL http://arxiv.org/abs/1606.01540.
Asaf Cassel and Tomer Koren. Bandit linear control. Advances in Neural Information Processing Systems, 33:8872-8882, 2020.
Asaf Cassel, Alon Cohen, and Tomer Koren. Rate-optimal online convex optimization in adaptive linear control. arXiv e-prints, pages arXiv-2206, 2022.
Xiaoyu Chen, Jiachen Hu, Chi Jin, Lihong Li, and Liwei Wang. Understanding domain randomization for sim-to-real transfer. arXiv preprint arXiv:2110.03239, 2021a.
Xinyi Chen, Edgar Minasyan, Jason D Lee, and Elad Hazan. Provable regret bounds for deep online learning and control. arXiv preprint arXiv:2110.07807, 2021b.
Yan Duan, Xi Chen, Rein Houthooft, John Schulman, and Pieter Abbeel. Benchmarking deep reinforcement learning for continuous control. In International conference on machine learning, pages 1329-1338. PMLR, 2016.
Udaya Ghai, David Snyder, Anirudha Majumdar, and Elad Hazan. Generating adversarial disturbances for controller verification. In Ali Jabbabaie, John Lygeros, George J. Pappas, Pablo A. Parrilo, Benjamin Recht, Claire J. Tomlin, and Melanie N. Zeilinger, editors, Proceedings of the 3rd Conference on Learning for Dynamics and Control, volume 144 of Proceedings of Machine Learning Research, pages 1192-1204. PMLR, 07 - 08 June 2021.
Udaya Ghai, Udari Madhushani, Naomi Leonard, and Elad Hazan. A regret minimization approach to multi-agent control. In International Conference on Machine Learning, pages 7422-7434. PMLR, 2022.
Ian J Goodfellow, Jonathon Shlens, and Christian Szegedy. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572, 2014.
Paula Gradu, John Hallman, and Elad Hazan. Non-stochastic control with bandit feedback. Advances in Neural Information Processing Systems, 33:10764-10774, 2020.
Paula Gradu, John Hallman, Daniel Suo, Alex Yu, Naman Agarwal, Udaya Ghai, Karan Singh, Cyril Zhang, Anirudha Majumdar, and Elad Hazan. Deluca-a differentiable control library: Environments, methods, and benchmarking. arXiv preprint arXiv:2102.09968, 2021.
David Ha and Jürgen Schmidhuber. World models. arXiv preprint arXiv:1803.10122, 2018.
Elad Hazan and Karan Singh. Introduction to online nonstochastic control. arXiv preprint arXiv:2211.09619, 2022.
Elad Hazan, Sham Kakade, and Karan Singh. The nonstochastic control problem. In *Algorithmic Learning Theory*, pages 408-421. PMLR, 2020.
Elad Hazan et al. Introduction to online convex optimization. Foundations and Trends® in Optimization, 2(3-4):157-325, 2016.