traffic light control using deep policy-gradient and value-function based reinforcement learning
Mousavi, Seyed Sajad
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Mousavi, Seyed Sajad; schukat, Michael; Howley, Enda (2017). traffic light control using deep policy-gradient and value-function based reinforcement learning . IET Intelligent Transport Systems 11 (7), 417-423
Recent advances in combining deep neural network architectures with reinforcement learning (RL) techniques have shown promising potential results in solving complex control problems with high-dimensional state and action spaces. Inspired by these successes, in this study, the authors built two kinds of RL algorithms: deep policy-gradient (PG) and value-function-based agents which can predict the best possible traffic signal for a traffic intersection. At each time step, these adaptive traffic light control agents receive a snapshot of the current state of a graphical traffic simulator and produce control signals. The PG-based agent maps its observation directly to the control signal; however, the value-function-based agent first estimates values for all legal control signals. The agent then selects the optimal control action with the highest value. Their methods show promising results in a traffic network simulated in the simulation of urban mobility traffic simulator, without suffering from instability issues during the training process.
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