Deep Reinforcement Learning (DRL) has become increasingly powerful in recent years, with notable achievements such as Deepmind’s AlphaGo. It has been successfully deployed in commercial vehicles like Mobileye’s path ...

Multi-Agent Systems (MAS) are a form of distributed intelligence, where multiple autonomous agents act in a common environment. Numerous complex, real world systems have been successfully optimised using Multi-Agent ...

Correctly identifying vulnerable road users (VRUs), e.g. cyclists and pedestrians, remains one of the most challenging environment perception tasks for autonomous vehicles (AVs). This work surveys the current state-of-the-art ...

Recursive data structures are fundamental to the solution of many problems in computer science. In particular, recursive structures based on graph theory have been successfully applied to a diverse range of problems including ...

The majority ofMulti-Agent Reinforcement Learning (MARL) implementations aim to optimise systems with respect to a single objective, despite the fact that many real world problems are inherently multi-objective in nature. ...

Reward shaping is a well-established family of techniques that have been successfully used to improve the performance and learning speed of Reinforcement Learning agents in singleobjective problems. Here we extend the ...

Reward shaping has been proposed as a means to address the credit assignment problem in Multi-Agent Systems (MAS). Two popular shaping methods are Potential-Based Reward Shaping and di erence rewards, and both have been ...

Reinforcement Learning (RL) is a powerful and well-studied Machine Learning paradigm, where an agent learns to improve its performance in an environment by maximising a reward signal. In multi-objective Reinforcement Learning ...

In this paper,we leverage curriculum learning(CL) to improve the performance of multiagent systems(MAS) that are trained with the cooperative coevolution of artificial neural networks. We design curricula to progressively ...

In multi-objective multi-agent systems (MOMAS), agents explicitly consider the possible tradeoffs between conflicting objective functions. We argue that compromises between competing objectives in MOMAS should be analysed ...