School of Computer Science and Statistics
In reinforcement learning, agents usually process all inputs equally, even if some are more relevant than others. This project will explore the use of a simplified attention mechanism in a car-following environment, allowing a vehicle to focus more on important neighbours (such as the vehicle directly in front). Students will compare the performance of Multi-Agent … Read more
Reinforcement learning agents often behave conservatively or inconsistently at the start of training, leading to the so-called cold-start problem. This project will simulate a simple traffic scenario and train a multi-agent RL-based autonomous vehicle controller. Students will then explore strategies to reduce cold-start effects, such as imitation learning for initialization or reusing past experiences, and … Read more
Before fully autonomous driving is achieved, connected autonomous vehicles (CAVs) will operate for a certain period in mixed traffic, which includes both CAVs and human-driven vehicles (HDVs). The dynamic and interactive conditions in mixed traffic scenarios renders CAV decision making particularly challenging. This project will investigate the use of Graph Convolutional Deep Reinforcement Learning for … Read more
This project will explore applications of Reinforcement Learning (RL) for a real-world application in the railway domain. To address increasing demand, railway providers aim to increase traffic density on the existing network. However, dense traffic can result in delayed trains and infrastructure disruptions, which can impact planned trips in a large part of the network … Read more
Have experience with and your own FYP/MSc project idea related to Reinforcement Learning (“plain” or multi-objective, multi-agent, transfer, lifelong, explainable, inverse, combined with LLMs, 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.
Reinforcement learning (RL) has been successfully applied in a wide range of domains, demonstrating its potential to perform complex tasks by optimizing reward signals obtained through interaction with the environment. However, real-world tasks often involve multiple, potentially conflicting objectives that are not easily represented by a single scalar reward. Multi-Objective Reinforcement Learning (MORL) addresses this … Read more
Reinforcement learning (RL) and especially deep reinforcement learning have emerged as powerful paradigms for learning quasi-optimal sequential decision making strategies (in applications as diverse as game playing and urban traffic control). RL uses an approach based on’ trial and error’ in which agents learn by interacting with their environment. Most RL algorithms assume that the … Read more
Poor travel-time reliability, meaning that travel times for the same journey are highly variable and unpredictable, gives rise to similar negative impacts on the environment and the economy as does traffic congestion. Moreover, being able to offer a high degree of travel-time reliability will facilitate the uptake of sustainable road transportation including future public, shared, … Read more
An ideal bus route would offer highly predictable journey times to travellers so that the same journey taken at the same time of day on different occasions would take the same amount of time. While there are many sources of variability in journey times such as traffic conditions, passenger boarding and offloading time, and the … 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