20 citations · 39 across the 9 of their papers we have counts for
9 papers
Local Updates, Global Learning (LUGL): Playing Games with non-incremental Learners
David Milec, Spyridon Samothrakis, Michael Fairbank +1
The dominance of Neural Networks (NNs) in RL is partially due to their incremental learning capability, which naturally suits the online, non-stationary nature of self-play trainin…
A Comparison of Deep-Learning Methods for Analysing and Predicting Business Processes
Ishwar Venugopal, Jessica Töllich, Michael Fairbank +1
Deep-learning models such as Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) have been successfully used for process-mining tasks. They have achieved better p…
Deep Learning in Target Space
Michael Fairbank, Spyridon Samothrakis, Luca Citi
Deep learning uses neural networks which are parameterised by their weights. The neural networks are usually trained by tuning the weights to directly minimise a given loss functio…
Mek: Mechanics Prototyping Tool for 2D Tile-Based Turn-Based Deterministic Games
Rokas Volkovas, Michael Fairbank, John Woodward +1
There are few digital tools to help designers create game mechanics. A general language to express game mechanics is necessary for rapid game design iteration. The first iteration…
Optimal resampling for the noisy OneMax problem
Jialin Liu, Michael Fairbank, Diego Pérez-Liébana +1
The OneMax problem is a standard benchmark optimisation problem for a binary search space. Recent work on applying a Bandit-Based Random Mutation Hill-Climbing algorithm to the noi…
The Importance of Clipping in Neurocontrol by Direct Gradient Descent on the Cost-to-Go Function and in Adaptive Dynamic Programming
Michael Fairbank
In adaptive dynamic programming, neurocontrol and reinforcement learning, the objective is for an agent to learn to choose actions so as to minimise a total cost function. In this…