Multiagent systems (MAS) offer a powerful paradigm for modelling distributed settings that
require robust, scalable, and often decentralised control solutions. Despite its numerous …

Multi-agent systems (MAS) offer a powerful paradigm for modelling distributed settings that
require robust, scalable, and often decentralised control solutions. MAS applications vary …

The problem of traffic congestion incurs numerous social and economical repercussions and
has thus become a central issue in every major city in the world. For this work we look at the …

We consider the route choice problem using multiagent reinforcement learning. In this
problem, agents individually learn which routes minimise their expected travel costs. Such a …

Real-world congestion problems (eg traffic congestion) are typically very complex and large-
scale. Multiagent reinforcement learning (MARL) is a promising candidate for dealing with …

Connected and autonomous vehicle technology has advanced rapidly in recent years.
These technologies create possibilities for advanced AI-based traffic management …

In the micro-tolling paradigm, different toll values are assigned to different links within a
congestible traffic network. Self-interested agents then select minimal cost routes, where …

Many real-world systems such as taxi systems, traffic networks and smart grids involve self-
interested actors that perform individual tasks in a shared environment. However, in such …

Congestion problems are omnipresent in today's complex networks and represent a
challenge in many research domains. In the context of Multi-agent Reinforcement Learning …

Traffic congestion problems provide a unique environment to study how multi-agent systems
promote desired system level behavior. What is particularly interesting in this class of …