Multi-Agent Reinforcement Learning Based on Representational Communication for Large-Scale Traffic Signal Control

Reinforcement learning-based multi-agent system for network traffic signal control

A challenging application of artificial intelligence systems involves the scheduling of traffic signals in multi-intersection vehicular networks. This paper introduces a novel use of a multi-agent system and reinforcement learning (RL) framework to obtain an efficient traffic signal control policy. The latter is aimed at minimising the average delay, congestion and likelihood of intersection cr...

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 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 in Large Multi-agent Systems

Enabling reinforcement learning to be effective in large-scale multi-agent Markov Decisions Problems is a challenging task. To address this problem we propose a multi-agent variant of Q-learning: “Q Updates with Immediate Counterfactual Rewards-learning” (QUICR-learning). Given a global reward function over all agents that the large-scale system is trying to maximize, QUICR-learning breaks down...