404 citations · 828 across the 20 of their papers we have counts for
12 papers · 1 filter
Pittsburgh Learning Classifier Systems for Explainable Reinforcement Learning: Comparing with XCS
Jordan T. Bishop, Marcus Gallagher, Will N. Browne
Interest in reinforcement learning (RL) has recently surged due to the application of deep learning techniques, but these connectionist approaches are opaque compared with symbolic…
A Genetic Fuzzy System for Interpretable and Parsimonious Reinforcement Learning Policies
Jordan T. Bishop, Marcus Gallagher, Will N. Browne
Reinforcement learning (RL) is experiencing a resurgence in research interest, where Learning Classifier Systems (LCSs) have been applied for many years. However, traditional Michi…
Approximate discounting-free policy evaluation from transient and recurrent states
Vektor Dewanto, Marcus Gallagher
In order to distinguish policies that prescribe good from bad actions in transient states, we need to evaluate the so-called bias of a policy from transient states. However, we obs…
From Zero-Shot Machine Learning to Zero-Day Attack Detection
Mohanad Sarhan, Siamak Layeghy, Marcus Gallagher +1
The standard ML methodology assumes that the test samples are derived from a set of pre-observed classes used in the training phase. Where the model extracts and learns useful patt…
Examining average and discounted reward optimality criteria in reinforcement learning
Vektor Dewanto, Marcus Gallagher
In reinforcement learning (RL), the goal is to obtain an optimal policy, for which the optimality criterion is fundamentally important. Two major optimality criteria are average an…
A nearly Blackwell-optimal policy gradient method
Vektor Dewanto, Marcus Gallagher
For continuing environments, reinforcement learning (RL) methods commonly maximize the discounted reward criterion with discount factor close to 1 in order to approximate the avera…