TY - GEN
T1 - Framework for control and deep reinforcement learning in traffic
AU - Wu, Cathy
AU - Parvate, Kanaad
AU - Kheterpal, Nishant
AU - DIckstein, Leah
AU - Mehta, Ankur
AU - Vinitsky, Eugene
AU - Bayen, Alexandre M.
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2017/7/2
Y1 - 2017/7/2
N2 - Recent advances in deep reinforcement learning (RL) offer an opportunity to revisit complex traffic control problems at the level of vehicle dynamics, with the aim of learning locally optimal policies (with respect to the policy parameterization) for a variety of objectives such as matching a target velocity or minimizing fuel consumption. In this article, we present a framework called CISTAR (Customized Interface for SUMO, TraCI, and RLLab) that integrates the widely used traffic simulator SUMO with a standard deep reinforcement learning library RLLab. We create an interface allowing for easy customization of SUMO, allowing users to easily implement new controllers, heterogeneous experiments, and user-defined cost functions that depend on arbitrary state variables. We demonstrate the usage of CISTAR with several benchmark control and RL examples.
AB - Recent advances in deep reinforcement learning (RL) offer an opportunity to revisit complex traffic control problems at the level of vehicle dynamics, with the aim of learning locally optimal policies (with respect to the policy parameterization) for a variety of objectives such as matching a target velocity or minimizing fuel consumption. In this article, we present a framework called CISTAR (Customized Interface for SUMO, TraCI, and RLLab) that integrates the widely used traffic simulator SUMO with a standard deep reinforcement learning library RLLab. We create an interface allowing for easy customization of SUMO, allowing users to easily implement new controllers, heterogeneous experiments, and user-defined cost functions that depend on arbitrary state variables. We demonstrate the usage of CISTAR with several benchmark control and RL examples.
KW - Simulation
KW - control
KW - deep reinforcement learning
KW - vehicle dynamics
UR - http://www.scopus.com/inward/record.url?scp=85046287181&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85046287181&partnerID=8YFLogxK
U2 - 10.1109/ITSC.2017.8317694
DO - 10.1109/ITSC.2017.8317694
M3 - Conference contribution
AN - SCOPUS:85046287181
T3 - IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC
SP - 1
EP - 8
BT - 2017 IEEE 20th International Conference on Intelligent Transportation Systems, ITSC 2017
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 20th IEEE International Conference on Intelligent Transportation Systems, ITSC 2017
Y2 - 16 October 2017 through 19 October 2017
ER -