Fetching the paper…
Reading the bibliography…
Reinforcement learning (RL) is a powerful data-driven control method that has been largely explored in autonomous driving tasks.
L.-J. Lin, “Self-improving reactive agents based on reinforcement learning, planning and teaching,” Machine learning , vol. 8, no. 3-4, pp. 293–321, 1992
1992
Earlier work this paper cites.
H. K. Khalil, Nonlinear System . Prentice Hall, 2002
2002
Earlier work this paper cites.
2013
Earlier work this paper cites.
2013
Earlier work this paper cites.
2015
Earlier work this paper cites.
A. E. Sallab, M. Abdou, E. Perot, and S. Yogamani, “Deep reinforcement learning framework for autonomous driving,” Electronic Imaging , vol. 2017, no. 19, pp. 70–76, 2017
2017
Earlier work this paper cites.
2017
Earlier work this paper cites.
2017
Earlier work this paper cites.
B. Amos, L. Xu, and J. Z. Kolter, “Input convex neural networks,” in International Conference on Machine Learning . PMLR, 2017, pp. 146–155
2017
Cited alongside, same era.
P. Polack, F. Altché, B. d’Andréa Novel, and A. de La Fortelle, “The kinematic bicycle model: A consistent model for planning feasible trajectories for autonomous vehicles?” in 2017 IEEE intelligent vehicles symposium (IV) . IEEE, 2017, pp. 812–818
2017
Cited alongside, same era.
2018
Cited alongside, same era.
T. Xu, Q. Liu, L. Zhao, and J. Peng, “Learning to explore via meta-policy gradient,” in International Conference on Machine Learning , 2018, pp. 5463–5472
2018
Cited alongside, same era.
Y.-C. Chang, N. Roohi, and S. Gao, “Neural lyapunov control,” in Advances in Neural Information Processing Systems , 2019, pp. 3245–3254
2019
Later among the works it cites.
2019
Later among the works it cites.
D. Chen, L. Jiang, Y. Wang, and Z. Li, “Autonomous driving using safe reinforcement learning by incorporating a regret-based human lane-changing decision model,” in 2020 American Control Conference (ACC) . IEEE, 2020, pp. 4355–4361
2020
Later among the works it cites.
2020
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
2018
Cited alongside, same era.
E. Leurent, “An environment for autonomous driving decision-making,” https://github.com/eleurent/highway-env , 2018
2018
Cited alongside, same era.
2019
Cited alongside, same era.
2019
Cited alongside, same era.
T. Ardoin, E. Paris-Saclay, E. Vinitsky, and A. Bayen, “Extracting traffic smoothing controllers directly from driving data using offline rl.”
Cited in the paper.
2020
Later among the works it cites.
J. Chen, S. E. Li, and M. Tomizuka, “Interpretable end-to-end urban autonomous driving with latent deep reinforcement learning,” IEEE Transactions on Intelligent Transportation Systems , 2021
2021
Closest in time.
2021
Closest in time.
S. Fujimoto, D. Meger, and D. Precup, “Off-policy deep reinforcement learning without exploration,” in International Conference on Machine Learning . PMLR, 2019, pp. 2052–2062
2062
Closest in time.