Pain: A Precision Signal for Reinforcement Learning and Control

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...

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 in neurofuzzy traffic signal control

Reinforcement Learning for Control

Reinforcement learning (RL) offers a principled way to control nonlinear stochastic systems with partly or even fully unknown dynamics. Recent advances in areas such as deep learning and adaptive dynamic programming (ADP) have led to significant inroads in applications from robotics, automotive systems, smart grids, game playing, traffic control, etc. This open track provides a forum of interac...

A Reinforcement Learning Variant for Control Scheduling

We present an algorithm based on reinforcement and state recurrence learning techniques to solve control scheduling problems. In particular, we have devised a simple learning scheme called "handicapped learning", in which the weights of the associative search element are reinforced, either positively or negatively, such that the system is forced to move towards the desired setpoint in the short...