Now showing items 1-5 of 5

    • 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 ...
    • 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 ...
    • 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 ...
    • 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 ...