Publications (29)
Optimality-based Analysis of XCSF Compaction in Discrete Reinforcement Learning
Jordan T. Bishop, Marcus Gallagher
Learning classifier systems (LCSs) are population-based predictive systems that were originally envisioned as agents to act in reinforcement learning (RL) environments. These syste…
Avoiding Kernel Fixed Points: Computing with ELU and GELU Infinite Networks
Russell Tsuchida, Tim Pearce, Chris van der Heide +2
Analysing and computing with Gaussian processes arising from infinitely wide neural networks has recently seen a resurgence in popularity. Despite this, many explicit covariance fu…
Average-reward model-free reinforcement learning: a systematic review and literature mapping
Vektor Dewanto, George Dunn, Ali Eshragh +2
Reinforcement learning is important part of artificial intelligence. In this paper, we review model-free reinforcement learning that utilizes the average reward optimality criterio…
Invariance of Weight Distributions in Rectified MLPs
Russell Tsuchida, Farbod Roosta-Khorasani, Marcus Gallagher
An interesting approach to analyzing neural networks that has received renewed attention is to examine the equivalent kernel of the neural network. This is based on the fact that a…
Richer priors for infinitely wide multi-layer perceptrons
Russell Tsuchida, Fred Roosta, Marcus Gallagher
It is well-known that the distribution over functions induced through a zero-mean iid prior distribution over the parameters of a multi-layer perceptron (MLP) converges to a Gaussi…
Hyperparameter Optimisation with Practical Interpretability and Explanation Methods in Probabilistic Curriculum Learning
Llewyn Salt, Marcus Gallagher
Hyperparameter optimisation (HPO) is crucial for achieving strong performance in reinforcement learning (RL), as RL algorithms are inherently sensitive to hyperparameter settings.…