Multi-Agent Reinforcement Leraning for Traffic Light Control

By Marco A. Wiering. International Conference on Machine Learning, 2000

305 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/7ddab7553463a11d1774d868f6b82a8360b8a63e

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Citations in the corpus

Hierarchical Multiagent Reinforcement Learning for Maritime Traffic Management (2020, methodology): “Credit assignment with meta actions: To address the multiagent credit assignment problem, we propose a solution approach motivated from car-based value function used for traffic light control [34].”
Computational Intelligence in Urban Traffic Signal Control: A Survey (2012, methodology): “[38], [39] used a model-based RL method in a multiagent-based TSC.”
Multiagent Reinforcement Learning for Integrated Network of Adaptive Traffic Signal Controllers (MARLIN-ATSC): Methodology and Large-Scale Application on Downtown Toronto (2013, methodology): “[29] found the only algorithm, to the best of authors’ knowledge, that considered an explicit coordination mechanism between learning agents, extending the work of Wiering in [19] using the Max-plus algorithm.”
LIIR: Learning Individual Intrinsic Reward in Multi-Agent Reinforcement Learning (2019, background): “Many real-world problems, such as traffic light control [1], coordination of autonomous vehicles [2], resources management [3] and multi-player video games [4, 5], can be naturally formulated into cooperative multi-agent systems, where the objective is to maximize the return in the perspective of a ”
Multi-Agent Deep Reinforcement Learning for Large-Scale Traffic Signal Control (2019, methodology): “Reference [34] applied model-based tabular IQL to each intersection while [35] extended LR IQL”
A review on agent-based technology for traffic and transportation (2013, background): “As in Wiering (2000), it is assumed that the traffic signals are able to know the value functions of vehicles around (plus information such as destination).”
Natural Actor-Critic for Road Traffic Optimisation (2006, background): “Reinforcement learning has also been applied [6], but in a way that uses a value function for each car, which is unrealistic in today’s world.”
Graph Convolutional Reinforcement Learning (2018, background): “It is crucially important to enable agents to learn to cooperate in multi-agent environments for many applications, e.g. , autonomous driving (Shalev-Shwartz et al., 2016), trafﬁc light control (Wiering, 2000), smart grid control (Yang et al., 2018a), and multi-robot control (Matignon et al., 2012).”
Multi-agent Reinforcement Learning: An Overview (2010, background): “Particular distributed control domains where MARL is applied are process control [113], control of traffic signals [4, 141], and control of electrical power networks [100].”
A Survey on Traffic Signal Control Methods (2019, methodology): “hamis and Gomaa 2012; Khamis et al. 2012; Kuyer et al. 2008; Mannion et al. 2016; Prashanth and Bhatnagar 2011; Salkham et al. 2008; Steingrover et al. 2005; van der Pol et al. 2016; Wei et al. 2018; Wiering 2000; Wiering et al. 2004a,b] Waiting time [Bakker et al. 2010; Brys et al. 2014; Chu et al.”
Deep Reinforcement Learning for Intelligent Transportation Systems: A Survey (2020, methodology): “Multi-objective learning 12 mixed intersections GLD TC-1 [65]”
A Comprehensive Survey of Multiagent Reinforcement Learning (2008, unknown): “Particular distributed control domains where MARL is applied are process control [90], control of traffic signals [91], [92], and control of electrical power networks [93].”
Empirical Study of Off-Policy Policy Evaluation for Reinforcement Learning (2019, background): “These new developments reflect practical interests in deploying reinforcement learning to safety-critical situations (Li et al., 2011; Wiering, 2000; Bottou et al., 2013; Bang & Robins, 2005), and the increasing importance of off-policy learning and counterfactual reasoning more broadly (Degris et a”
Succinct and Robust Multi-Agent Communication With Temporal Message Control (2020, unknown): “Multi-agent reinforcement learning (MARL) has achieved remarkable success in a variety of challenging problems, including intelligent traffic signal control [36], swarm robotics [15], autonomous driving [28].”