1 citations · 3 across the 5 of their papers we have counts for
5 papers
Bridging Reinforcement Learning and Optimal Control via Feasible Action Mapping
Stefan Richter, Alberto Giammarino, Guillem Torrente +2
Operating constrained dynamical systems requires controllers to efficiently solve complex tasks while enforcing recursive feasibility and physical constraints. To address these com…
Federated Transformed Learning for a Circular, Secure, and Tiny AI
Weisi Guo, Schyler Sun, Bin Li +1
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. DL is at the hear…
Generating Explanations from Deep Reinforcement Learning Using Episodic Memory
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. For many tasks the resulting sequence of…
Selective Particle Attention: Visual Feature-Based Attention in Deep Reinforcement Learning
Sam Blakeman, Denis Mareschal
The human brain uses selective attention to filter perceptual input so that only the components that are useful for behaviour are processed using its limited computational resource…
A Complementary Learning Systems Approach to Temporal Difference Learning
Sam Blakeman, Denis Mareschal
Complementary Learning Systems (CLS) theory suggests that the brain uses a 'neocortical' and a 'hippocampal' learning system to achieve complex behavior. These two systems are comp…