Continuous residual reinforcement learning for traffic signal control optimization

Reinforcement Learning For Adaptive Traffic Signal Control

By 2050, two-thirds of the world’s 9.6 billion people will live in urban areas [2]. In many cities, opportunities to expand urban road networks are limited, so existing roads will need to more efficiently accommodate higher volumes of traffic. Consequently, there is a pressing need for technologically viable, low-cost solutions that can work with existing infrastructure to help alleviate increa...

Reinforcement learning in neurofuzzy traffic signal control

Reinforcement Learning for Traffic Optimization

In this paper we apply reinforcement learning techniques to traffic light policies with the aim of increasing traffic flow through intersections. We model intersections with states, actions, and rewards, then use an industry-standard software platform to simulate and evaluate different policies against them. We compare various policies including fixed cycles, longest queue first (LQF), and the ...

Using a Deep Reinforcement Learning Agent for Traffic Signal Control

Ensuring transportation systems are efficient is a priority for modern society. Technological advances have made it possible for transportation systems to collect large volumes of varied data on an unprecedented scale. We propose a traffic signal control system which takes advantage of this new, high quality data, with minimal abstraction compared to other proposed systems. We apply modern deep...

Reinforcement Learning for Continuous Stochastic Control Problems

This paper is concerned with the problem of Reinforcement Learning (RL) for continuous state space and time stocha.stic control problems. We state the Harnilton-Jacobi-Bellman equation satisfied by the value function and use a Finite-Difference method for designing a convergent approximation scheme. Then we propose a RL algorithm based on this scheme and prove its convergence to the optimal sol...