Federated Transformed Learning for a Circular, Secure, and Tiny AI

no code implementations • 24 Nov 2023 • Weisi Guo, Schyler Sun, Bin Li, Sam Blakeman

Deep Learning (DL) is penetrating into a diverse range of mass mobility, smart living, and industrial applications, rapidly transforming the way we live and work.

Generating Explanations from Deep Reinforcement Learning Using Episodic Memory

no code implementations • 18 May 2022 • Sam Blakeman, Denis Mareschal

Deep Reinforcement Learning (RL) involves the use of Deep Neural Networks (DNNs) to make sequential decisions in order to maximize reward.

reinforcement-learning Reinforcement Learning (RL)

Selective Particle Attention: Visual Feature-Based Attention in Deep Reinforcement Learning

no code implementations • 26 Aug 2020 • Sam Blakeman, Denis Mareschal

We focus on one particular form of visual attention known as feature-based attention, which is concerned with identifying features of the visual input that are important for the current task regardless of their spatial location.

Multiple-choice Open-Ended Question Answering +3

A Complementary Learning Systems Approach to Temporal Difference Learning

no code implementations • 7 May 2019 • Sam Blakeman, Denis Mareschal

In the present study we propose a novel algorithm known as Complementary Temporal Difference Learning (CTDL), which combines a DNN with a Self-Organising Map (SOM) to obtain the benefits of both a 'neocortical' and a 'hippocampal' system.

Reinforcement Learning (RL)