Flow: Architecture and Benchmarking for Reinforcement Learning in Traffic Control

By Cathy Wu, Aboudy Kreidieh, Kanaad Parvate, Eugene Vinitsky, Alexandre M. Bayen. 2017

223 citations recorded by the source corpus.

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Citations in the corpus

Deep Reinforcement Learning for Autonomous Driving: A Survey (2020, methodology): “Simulator Description CARLA [79] Urban simulator, Camera & LIDAR streams, with depth & semantic segmentation, Location information TORCS [103] Racing Simulator, Camera stream, agent positions, testing control policies for vehicles AIRSIM [104] Camera stream with depth and semantic segmentation, supp”
Deep Learning for Intelligent Transportation Systems: A Survey of Emerging Trends (2020, background): “Recent efforts to introduce similar capabilities within transportation can be seen in frameworks such as Flow [103], which attempts to integrate various environments such as SUMO [55] (Simulation of Urban MObility).”
Tackling Climate Change with Machine Learning (2019, background): “[217, 218] develop AV controllers based on reinforcement learning to smooth out traffic involving non-autonomous vehicles, reducing congestion-related energy consumption.”
Integrating Independent and Centralized Multi-agent Reinforcement Learning for Traffic Signal Network Optimization (2019, methodology): “We evaluated the performance of our method against a large set of baselines on multiple road networks under a variety of traffic conditions in the SUMO simulator [14, 25].”
Flow: A Modular Learning Framework for Mixed Autonomy Traffic (2017, methodology): “Since an early version of this manuscript was made available online in 2017 [93], several works have employed this framework to achieve results in the directions of the discovery of emergent behaviors in trafﬁc, transfer learning, bottleneck control, the design of trafﬁc control benchmarks, and…”
A review of cooperative multi-agent deep reinforcement learning (2019, background): “In addition, [151] introduce a framework to control cars within a mixed system of human-like driver and AI agents.”
A Survey on Offline Reinforcement Learning: Taxonomy, Review, and Open Problems (2022, methodology): “The D4RL benchmark includes datasets for OpenAI’s Gym-MuJoCo tasks, mazes, dexterous manipulation tasks (i.e., Adroit [68]), robotic manipulation tasks (i.e., FrankaKitchen [69]), autonomous driving (i.e., car learning to act (CARLA) [70]), and traffic simulation (i.e., flow [71]).”