Fetching the paper…
Reading the bibliography…
Deep Reinforcement Learning (DRL) has become increasingly powerful in recent years, with notable achievements such as Deepmind's AlphaGo.
Asynchronous methods for deep reinforcement learning
Mnih, V., Badia, A. P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., and Kavukcuoglu, K. (2016) · 1937
Earlier work this paper cites.
Integrated architectures for learning, planning, and reacting based on approximating dynamic programming
Sutton, R. S. (1990) · 1990
Earlier work this paper cites.
Meta-neural networks that learn by learning
Naik, D. K. and Mammone, R. (1992) · 1992
Earlier work this paper cites.
Robot learning from demonstration
Atkeson, C. G. and Schaal, S. (1997) · 1997
Earlier work this paper cites.
Policy invariance under reward transformations: Theory and application to reward shaping
Ng, A. Y., Harada, D., and Russell, S. (1999) · 1999
Earlier work this paper cites.
Torcs, the open racing car simulator
Wymann, B., Espié, E., Guionneau, C., Dimitrakakis, C., Coulom, R., and Sumner, A. (2000) · 2000
Earlier work this paper cites.
Recent advances in hierarchical reinforcement learning
Barto, A. G. and Mahadevan, S. (2003) · 2003
Earlier work this paper cites.
Design and use paradigms for gazebo, an open-source multi-robot simulator
Koenig, N. and Howard, A. (2004) · 2004
Earlier work this paper cites.
Exploration and apprenticeship learning in reinforcement learning
Abbeel, P. and Ng, A. Y. (2005) · 2005
Earlier work this paper cites.
Intrinsically motivated reinforcement learning
Chentanez, N., Barto, A. G., and Singh, S. P. (2005) · 2005
Earlier work this paper cites.
Sample-based learning and search with permanent and transient memories
Silver, D., Sutton, R. S., and Müller, M. (2008) · 2008
Earlier work this paper cites.
Maximum entropy inverse reinforcement learning
Ziebart, B. D., Maas, A. L., Bagnell, J. A., and Dey, A. K. (2008) · 2008
Earlier work this paper cites.
Transfer learning for reinforcement learning domains: A survey
Taylor, M. E. and Stone, P. (2009) · 2009
Earlier work this paper cites.
Planning-based prediction for pedestrians
Ziebart, B. D., Ratliff, N., Gallagher, G., Mertz, C., Peterson, K., Bagnell, J. A., Hebert, M., Dey, A. K., and Srinivasa, S. (2009) · 2009
Earlier work this paper cites.
Efficient reductions for imitation learning
Ross, S. and Bagnell, D. (2010) · 2010
Earlier work this paper cites.
A multiple-goal reinforcement learning method for complex vehicle overtaking maneuvers
Ngai, D. C. K. and Yung, N. H. C. (2011) · 2011
Earlier work this paper cites.
Are we ready for autonomous driving? the kitti vision benchmark suite
Geiger, A., Lenz, P., and Urtasun, R. (2012) · 2012
Earlier work this paper cites.
Activity forecasting
Kitani, K. M., Ziebart, B. D., Bagnell, J. A., and Hebert, M. (2012) · 2012
Earlier work this paper cites.
Playing atari with deep reinforcement learning
Mnih, V., Kavukcuoglu, K., Silver, D., Graves, A., Antonoglou, I., Wierstra, D., and Riedmiller, M. (2013) · 2013
Earlier work this paper cites.
Deterministic policy gradient algorithms
Silver, D., Lever, G., Heess, N., Degris, T., Wierstra, D., and Riedmiller, M. (2014) · 2014
Earlier work this paper cites.
Learning driving styles for autonomous vehicles from demonstration
Kuderer, M., Gulati, S., and Burgard, W. (2015) · 2015
Earlier work this paper cites.
Continuous control with deep reinforcement learning
Lillicrap, T. P., Hunt, J. J., Pritzel, A., Heess, N., Erez, T., Tassa, Y., Silver, D., and Wierstra, D. (2015) · 2015
Earlier work this paper cites.
Human-level control through deep reinforcement learning
Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A. A., Veness, J., Bellemare, M. G., Graves, A., Riedmiller, M., Fidjeland, A. K., Ostrovski, G., et al. (2015) · 2015
Earlier work this paper cites.
Trust region policy optimization
Schulman, J., Levine, S., Abbeel, P., Jordan, M., and Moritz, P. (2015) · 2015
Earlier work this paper cites.
End to end learning for self-driving cars
Bojarski, M., Testa, D. D., Dworakowski, D., Firner, B., Flepp, B., Goyal, P., Jackel, L. D., Monfort, M., Muller, U., Zhang, J., Zhang, X., Zhao, J., and Zieba, K. (2016) · 2016
Earlier work this paper cites.
Fast reinforcement learning via slow reinforcement learning
Duan, Y., Schulman, J., Chen, X., Bartlett, P. L., Sutskever, I., and Abbeel, P. (2016) · 2016
Cited alongside, same era.
Guided cost learning: Deep inverse optimal control via policy optimization
Finn, C., Levine, S., and Abbeel, P. (2016) · 2016
Cited alongside, same era.
Generative adversarial imitation learning
Ho, J. and Ermon, S. (2016) · 2016
Cited alongside, same era.
End-to-end training of deep visuomotor policies
Levine, S., Finn, C., Darrell, T., and Abbeel, P. (2016) · 2016
Cited alongside, same era.
Applying multi-agent reinforcement learning to watershed management
Mason, K., Mannion, P., Duggan, J., and Howley, E. (2016) · 2016
Cited alongside, same era.
A survey of motion planning and control techniques for self-driving urban vehicles
Proximal policy optimization algorithms
Schulman, J., Wolski, F., Dhariwal, P., Radford, A., and Klimov, O. (2017) · 2017
Later among the works it cites.
Formulation of deep reinforcement learning architecture toward autonomous driving for on-ramp merge
Wang, P. and Chan, C.-Y. (2017) · 2017
Later among the works it cites.
End-to-end learning of driving models from large-scale video datasets
Xu, H., Gao, Y., Yu, F., and Darrell, T. (2017) · 2017
Later among the works it cites.
Survival-oriented reinforcement learning model: An effcient and robust deep reinforcement learning algorithm for autonomous driving problem
Ye, C., Ma, H., Zhang, X., Zhang, K., and You, S. (2017) · 2017
Later among the works it cites.
Query-efficient imitation learning for end-to-end simulated driving
Zhang, J. and Cho, K. (2017) · 2017
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Paden, B., Cáp, M., Yong, S. Z., Yershov, D. S., and Frazzoli, E. (2016) · 2016
Cited alongside, same era.
End-to-end deep reinforcement learning for lane keeping assist
Sallab, A. E., Abdou, M., Perot, E., and Yogamani, S. (2016) · 2016
Cited alongside, same era.
Safe, multi-agent, reinforcement learning for autonomous driving
Shalev-Shwartz, S., Shammah, S., and Shashua, A. (2016) · 2016
Cited alongside, same era.
Learning to drive using inverse reinforcement learning and deep q-networks
Sharifzadeh, S., Chiotellis, I., Triebel, R., and Cremers, D. (2016) · 2016
Cited alongside, same era.
Mastering the game of go with deep neural networks and tree search
Silver, D., Huang, A., Maddison, C. J., Guez, A., Sifre, L., Van Den Driessche, G., Schrittwieser, J., Antonoglou, I., Panneershelvam, V., Lanctot, M., et al. (2016) · 2016
Cited alongside, same era.
Learning to reinforcement learn
Wang, J. X., Kurth-Nelson, Z., Tirumala, D., Soyer, H., Leibo, J. Z., Munos, R., Blundell, C., Kumaran, D., and Botvinick, M. (2016) · 2016
Cited alongside, same era.
Combining deep reinforcement learning and safety based control for autonomous driving
Xiong, X., Wang, J., Zhang, F., and Li, K. (2016) · 2016
Cited alongside, same era.
Burda, Y., Edwards, H., Pathak, D., Storkey, A., Darrell, T., and Efros, A. A. (2018) · 2018
Later among the works it cites.
Deep hierarchical reinforcement learning for autonomous driving with distinct behaviors
Chen, J., Wang, Z., and Tomizuka, M. (2018) · 2018
Later among the works it cites.
Motion prediction of traffic actors for autonomous driving using deep convolutional networks
Djuric, N., Radosavljevic, V., Cui, H., Nguyen, T., Chou, F.-C., Lin, T.-H., and Schneider, J. (2018) · 2018
Later among the works it cites.
Recurrent world models facilitate policy evolution
Ha, D. and Schmidhuber, J. (2018a) · 2018
Later among the works it cites.
Deep reinforcement learning that matters
Henderson, P., Islam, R., Bachman, P., Pineau, J., Precup, D., and Meger, D. (2018) · 2018
Later among the works it cites.
Policy optimization with demonstrations
Kang, B., Jie, Z., and Feng, J. (2018) · 2018
Later among the works it cites.
Kendall, A., Hawke, J., Janz, D., Mazur, P., Reda, D., Allen, J.-M., Lam, V.-D., Bewley, A., and Shah, A. (2018) · 2018
Later among the works it cites.
Hierarchical imitation and reinforcement learning
Le, H. M., Jiang, N., Agarwal, A., Dudík, M., Yue, Y., and Daumé III, H. (2018) · 2018
Later among the works it cites.
Simple random search provides a competitive approach to reinforcement learning
Mania, H., Guy, A., and Recht, B. (2018) · 2018
Later among the works it cites.
On first-order meta-learning algorithms
Nichol, A., Achiam, J., and Schulman, J. (2018) · 2018
Later among the works it cites.
Towards practical hierarchical reinforcement learning for multi-lane autonomous driving
Nosrati, M. S., Abolfathi, E. A., Elmahgiubi, M., Yadmellat, P., Luo, J., Zhang, Y., Yao, H., Zhang, H., and Jamil, A. (2018) · 2018
Later among the works it cites.
Toward driving scene understanding: A dataset for learning driver behavior and causal reasoning
Ramanishka, V., Chen, Y.-T., Misu, T., and Saenko, K. (2018) · 2018
Later among the works it cites.
Planning and decision-making for autonomous vehicles
Schwarting, W., Alonso-Mora, J., and Rus, D. (2018) · 2018
Later among the works it cites.
Airsim: High-fidelity visual and physical simulation for autonomous vehicles
Shah, S., Dey, D., Lovett, C., and Kapoor, A. (2018) · 2018
Later among the works it cites.
End-to-end multi-modal sensors fusion system for urban automated driving
Sobh, I., Amin, L., Abdelkarim, S., Elmadawy, K., Saeed, M., Abdeltawab, O., Gamal, M., and El Sallab, A. (2018) · 2018
Later among the works it cites.
End-to-end framework for fast learning asynchronous agents
Sobh, I. and Darwish, N. (2018) · 2018
Later among the works it cites.
Reinforcement learning: An introduction
Sutton, R. S. and Barto, A. G. (2018) · 2018
Later among the works it cites.
A reinforcement learning based approach for automated lane change maneuvers
Wang, P., Chan, C.-Y., and de La Fortelle, A. (2018) · 2018
Later among the works it cites.
Domain-adversarial training of neural networks
Ganin, Y., Ustinova, E., Ajakan, H., Germain, P., Larochelle, H., Laviolette, F., Marchand, M., and Lempitsky, V. (2016) · 2030
Closest in time.
Deep reinforcement learning with double q-learning
Van Hasselt, H., Guez, A., and Silver, D. (2016) · 2094
Closest in time.