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Traffic congestion in metropolitan areas is a world-wide problem that can be ameliorated by traffic lights that respond dynamically to real-time conditions.
Multi-agent reinforcement learning: Independent vs. cooperative agents
Tan, M. (1993) · 1993
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Adaptive look-ahead optimization of traffic signals
Porche, I. and Lafortune, S. (1999) · 1999
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Large-scale traffic simulations for transportation planning
Esser, K. N. J. and Rickert, M. (2000) · 2000
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Policy gradient methods for reinforcement learning with function approximation
Sutton, R. S., McAllester, D. A., Singh, S. P., and Mansour, Y. (2000) · 2000
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The complexity of decentralized control of markov decision processes
Bernstein, D. S., Givan, R., Immerman, N., and Zilberstein, S. (2002) · 2002
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Reinforcement learning for true adaptive traffic signal control
Abdulhai, B., Pringle, R., and Karakoulas, G. J. (2003) · 2003
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Self-organizing traffic lights
Gershenson, C. (2004) · 2004
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Macroscopic modeling of traffic in cities
Geroliminis, N., Daganzo, C. F., et al · 2007
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Reinforcement learning-based multi-agent system for network traffic signal control
Arel, I., Liu, C., Urbanik, T., and Kohls, A. (2010) · 2010
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Urban traffic signal control using reinforcement learning agents
Balaji, P., German, X., and Srinivasan, D. (2010) · 2010
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Q-learning based traffic optimization in management of signal timing plan
Chin, Y. K., Bolong, N., Kiring, A., Yang, S. S., and Teo, K. T. K. (2011) · 2011
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Self-organizing traffic lights: A realistic simulation
Cools, S.-B., Gershenson, C., and D’Hooghe, B. (2013) · 2013
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Multiagent reinforcement learning for integrated network of adaptive traffic signal controllers (marlin-atsc): methodology and large-scale application on downtown toronto
El-Tantawy, S., Abdulhai, B., and Abdelgawad, H. (2013) · 2013
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Traffic congestion costs americans $124 billion a year, report says
Guerrini, F. (2014) · 2014
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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
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Value-decomposition networks for cooperative multi-agent learning
Sunehag, P., Lever, G., Gruslys, A., Czarnecki, W. M., Zambaldi, V., Jaderberg, M., Lanctot, M., Sonnerat, N., Leibo, J. Z., Tuyls, K., et al · 2017
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Multiagent cooperation and competition with deep reinforcement learning
Tampuu, A., Matiisen, T., Kodelja, D., Kuzovkin, I., Korjus, K., Aru, J., Aru, J., and Vicente, R. (2017) · 2017
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Flow: Architecture and benchmarking for reinforcement learning in traffic control
Wu, C., Kreidieh, A., Parvate, K., Vinitsky, E., and Bayen, A. M. (2017) · 2017
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Counterfactual multi-agent policy gradients
Foerster, J. N., Farquhar, G., Afouras, T., Nardelli, N., and Whiteson, S. (2018) · 2018
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Deep reinforcement learning for traffic light control in vehicular networks
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Genders, W. and Razavi, S. (2016) · 2016
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Traffic signal timing via deep reinforcement learning
Li, L., Lv, Y., and Wang, F.-Y. (2016) · 2016
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Coordinated deep reinforcement learners for traffic light control
Van der Pol, E. and Oliehoek, F. A. (2016) · 2016
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Cooperative deep reinforcement learning for tra ic signal control
LIU, M., DENG, J., XU, M., ZHANG, X., and WANG, W. (2017) · 2017
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Intelligent traffic light control using distributed multi-agent q learning
Liu, Y., Liu, L., and Chen, W.-P. (2017) · 2017
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Traffic light control using deep policy-gradient and value-function-based reinforcement learning
Mousavi, S. S., Schukat, M., and Howley, E. (2017) · 2017
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Liang, X., Du, X., Wang, G., and Han, Z. (2018) · 2018
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Microscopic traffic simulation using sumo
Lopez, P. A., Behrisch, M., Bieker-Walz, L., Erdmann, J., Flötteröd, Y.-P., Hilbrich, R., Lücken, L., Rummel, J., Wagner, P., and Wießner, E. (2018) · 2018
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QMIX: Monotonic value function factorisation for deep multi-agent reinforcement learning
Rashid, T., Samvelyan, M., Schroeder, C., Farquhar, G., Foerster, J., and Whiteson, S. (2018) · 2018
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Reinforcement learning: An introduction
Sutton, R. S. and Barto, A. G. (2018) · 2018
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Intellilight: A reinforcement learning approach for intelligent traffic light control
Wei, H., Zheng, G., Yao, H., and Li, Z. (2018) · 2018
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Cm3: Cooperative multi-goal multi-stage multi-agent reinforcement learning
Yang, J., Nakhaei, A., Isele, D., Zha, H., and Fujimura, K. (2018) · 2018
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Americans will waste $2.8 trillion on traffic by 2030 if gridlock persists
McNew, L. (2014) · 2030
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