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
The Research Ireland-funded ClearWay project [2] is investigating new models of road management is which each vehicle is allocated a slot in which to travel for the duration of its journey. Adherence to travelling in its allocated slot ensures congestion-free travel from source to destination for each vehicle (in the absence of unexpected events). A … Read more
Connected Autonomous Vehicles (CAVs) are expected to share the roads with Human Driven Vehicles for the foreseeable. The random sequences in this mixed traffic flow render the design of CAV controllers particularly challenging. This project investigates the design of a deep reinforcement learning algorithm algorithms to reduce the training dimensions and alleviate computational burdens. This … Read more