Meta Reinforcement Learning-Based Lane Change Strategy for Autonomous Vehicles

R. J. Williams, “Simple statistical gradient-following algorithms for connectionist reinforcement learning,” Machine learning , vol. 8, no. 3-4, pp. 229–256, 1992

1992

Earlier work this paper cites.

M. Treiber, A. Hennecke, and D. Helbing, “Congested traffic states in empirical observations and microscopic simulations,” Physical Review E , vol. 62, no. 2, p. 1805–1824, Aug 2000

2000

Earlier work this paper cites.

S. Kakade and J. Langford, “Approximately optimal approximate reinforcement learning,” in ICML , vol. 2, 2002, pp. 267–274

2002

Earlier work this paper cites.

M. Behrisch, L. Bieker, J. Erdmann, and D. Krajzewicz, “Sumo–simulation of urban mobility: an overview,” in Proceedings of SIMUL 2011, The Third International Conference on Advances in System Simulation . ThinkMind, 2011

2011

Earlier work this paper cites.

D. P. Kingma and J. Ba, “Adam: A method for stochastic optimization,” arXiv preprint arXiv:1412.6980 , 2014

Original

2014

Earlier work this paper cites.

D. González, J. Pérez, V. Milanés, and F. Nashashibi, “A review of motion planning techniques for automated vehicles,” IEEE Transactions on Intelligent Transportation Systems , vol. 17, no. 4, pp. 1135–1145, 2015

2015

Earlier work this paper cites.

B. Paden, M. Čáp, S. Z. Yong, D. Yershov, and E. Frazzoli, “A survey of motion planning and control techniques for self-driving urban vehicles,” IEEE Transactions on intelligent vehicles , vol. 1, no. 1, pp. 33–55, 2016

2016

Earlier work this paper cites.

J. Zhang and K. Cho, “Query-efficient imitation learning for end-to-end autonomous driving,” 2016

2016

Earlier work this paper cites.

Y. Duan, J. Schulman, X. Chen, P. L. Bartlett, I. Sutskever, and P. Abbeel, “Rl 2 : Fast reinforcement learning via slow reinforcement learning,” arXiv preprint arXiv:1611.02779 , 2016

Original

2016

Earlier work this paper cites.

J. X. Wang, Z. Kurth-Nelson, D. Tirumala, H. Soyer, J. Z. Leibo, R. Munos, C. Blundell, D. Kumaran, and M. Botvinick, “Learning to reinforcement learn,” arXiv preprint arXiv:1611.05763 , 2016

Original

2016

Earlier work this paper cites.

A. Sallab, M. Abdou, E. Perot, and S. Yogamani, “Deep reinforcement learning framework for autonomous driving,” Electronic Imaging , vol. 2017, p. 70–76, Jan 2017

2017

Cited alongside, same era.

A. E. Sallab, M. Saeed, O. A. Tawab, and M. Abdou, “Meta learning framework for automated driving,” 2017

2017

Cited alongside, same era.

C. Finn, P. Abbeel, and S. Levine, “Model-agnostic meta-learning for fast adaptation of deep networks,” arXiv preprint arXiv:1703.03400 , 2017

Original

2017

Cited alongside, same era.

J. Schulman, F. Wolski, P. Dhariwal, A. Radford, and O. Klimov, “Proximal policy optimization algorithms,” arXiv preprint arXiv:1707.06347 , 2017

Original

2017

Cited alongside, same era.

W. Saunders, G. Sastry, A. Stuhlmueller, and O. Evans, “Trial without error: Towards safe reinforcement learning via human intervention,” arXiv preprint arXiv:1707.05173 , 2017

B. Mirchevska, C. Pek, M. Werling, M. Althoff, and J. Boedecker, “High-level decision making for safe and reasonable autonomous lane changing using reinforcement learning,” in 2018 21st International Conference on Intelligent Transportation Systems (ITSC) , 2018, pp. 2156–2162

2018

Later among the works it cites.

A. Gupta, R. Mendonca, Y. Liu, P. Abbeel, and S. Levine, “Meta-reinforcement learning of structured exploration strategies,” in Advances in Neural Information Processing Systems , 2018, pp. 5302–5311

2018

Later among the works it cites.

A. Nagabandi, I. Clavera, S. Liu, R. S. Fearing, P. Abbeel, S. Levine, and C. Finn, “Learning to adapt in dynamic, real-world environments through meta-reinforcement learning,” arXiv preprint arXiv:1803.11347 , 2018

Original

2018

Later among the works it cites.

E. Yurtsever, J. Lambert, A. Carballo, and K. Takeda, “A survey of autonomous driving: common practices and emerging technologies,” arXiv preprint arXiv:1906.05113 , 2019

Original

2019