Recent Submissions

  • Aquaculture with a Focus on Vietnam & Thailand 

    Nash, Róisín; Shibaev, Sergey; Petkam, Rakpong; TUNASIA; Erasmus+ (TUNASIA, 2021)
    The breadth of material this book covers includes wide range of issues related to aquaculture production, including its development history, farming systems and technology aspects, seed production, feed and feeding, health ...
  • Assessing the Potential of Drones to Take Water Samples and Physicochemical Data from Open Lakes 

    Lally, Heather; O'Connor, Ian; Broderick, Liam; Broderick, Mark; Jensen, Olaf; Graham, Conor (Environmental Protection Agency, 2020-09)
    Water sampling remains a pivotal method for monitoring and understanding the condition of aquatic environments properly and effectively. Large-scale ecological water sampling and monitoring programmes require considerable ...
  • Phlorotannins and Macroalgal Polyphenols: Potential As Functional Food Ingredients and Role in Health Promotion 

    Ryan, Lisa; Murray, Margaret; Dordevic, Aimee L.; Bonham, Maxine P. (Springer, Singapore, 2018-01)
    Marine macroalgae are rapidly gaining recognition as a source of functional ingredients that can be used to promote health and prevent disease. There is accumulating evidence from in vitro studies, animal models, and ...
  • Vulnerable road user detection: state-of-the-art and open challenges 

    Mannion, Patrick (2019)
    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 ...
  • Curriculum Learning for Tightly Coupled Multiagent Systems 

    Mannion, Patrick; Rockefeller, Golden; Turner, Kagan (2019)
    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 ...
  • Multi-Agent Credit Assignment in Stochastic Resource Management Games 

    Mannion, Patrick; Devlin, Sam; Duggan, Jim; Howley, Enda (2017-08)
    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 ...
  • Potential-Based Reward Shaping Preserves Pareto Optimal Policies 

    Mannion, Patrick; Devlin, Sam; Karl, Mannion; Duggan, Jim (2017-05)
    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 ...
  • Policy Invariance under Reward Transformations for Multi-Objective Reinforcement Learning 

    Mannion, Patrick; Devlin, Sam; Mason, Karl; Duggan, Jim (2017)
    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 ...