School of Computer Science and Statistics
Have experience with and your own FYP/MSc project idea related to Reinforcement Learning (“plain” or multi-objective, multi-agent, transfer, lifelong, explainable, inverse, etc?) – whether to develop new algorithms or to apply existing ones to a new application area? Contact me with your idea to see if we can formulate the topic together.
This project will explore use of and development of new multi-agent RL techniques to achieve cooperation and coordination between autonomous vehicles in order to optimize traffic congestion. Prior experience with RL is essential
This project will apply and extend multi-objective (MO) technique Deep W-Networks[1] to a number of benchmark environments from multi-objective gym [2], and benchmark against other MO RL techniques. References: [1] J Hribar, L Hackett, I Dusparic. Deep W-Networks: Solving Multi-Objective Optimisation Problems With Deep Reinforcement Learning. International Conference on Agents and Artificial Intelligence (ICAART) 2023 … Read more
This project will investigate existing software testing techniques and development of new ones for testing of reinforcement learning-based software applications. References: [1] Yuteng Lu, Weidi Sun, Meng Sun, Towards mutation testing of Reinforcement Learning systems, Journal of Systems Architecture, Volume 131, 2022, https://www.sciencedirect.com/science/article/pii/S1383762122001977 [2] Miller Trujillo, Mario Linares-Vásquez, Camilo Escobar-Velásquez, Ivana Dusparic, and Nicolás Cardozo. … Read more
So-called ‘capacity drop’ is a phenomenon that sometimes occurs when joining vehicles interrupt the flow of traffic on a motorway, effectively reducing the capacity of the road. To address this problem, this project will investigate the design of a cooperative motorway on-ramp merging algorithm for automated vehicles. From the perspective of vehicles joining the motorway, … Read more
This project will investigate the use of reinforcement learning to develop a highway journey booking system incorporating a dynamic pricing strategy to allow traffic demand to be shaped in ways that will improve traffic efficiency and enhance sustainability by reducing emissions and fuel consumption. The goal will be to evaluate the potential benefit of the … Read more
Large-scale deployment of (semi-)automated vehicles (AVs) is inevitable. However, the benefits of this deployment for traffic management in a world in which AVs and other vehicles will necessarily coexist (i.e., in ‘mixed’ traffic) remain unclear. Reduced congestion, greater energy efficiency, and improved resilience of the traffic system to unexpected events are expected. In this context, … Read more
Transformer neural nets have transformed natural language processing, but they can also be applied to other sequences not just sequences of words. In particular, they can be applied to game playing, which consists of a sequence of moves and where the task is to predict a good next move. In this project you will investigate … Read more
Explanations targeted at non-expert users of AI systems are necessary to encourage collaboration and ensure user trust in the black-box system. Counterfactuals are user-friendly explanations that offer the user actionable advice on how to change their input features in order to achieve a desired output. While researched in depth in supervised learning, counterfactual explanationsare seldom … Read more