11 citations · 11 across the 2 of their papers we have counts for
5 papers
Action Advising with Advice Imitation in Deep Reinforcement Learning
Ercument Ilhan, Jeremy Gow, Diego Perez-Liebana
Action advising is a peer-to-peer knowledge exchange technique built on the teacher-student paradigm to alleviate the sample inefficiency problem in deep reinforcement learning. Re…
Learning on a Budget via Teacher Imitation
Ercument Ilhan, Jeremy Gow, Diego Perez-Liebana
Deep Reinforcement Learning (RL) techniques can benefit greatly from leveraging prior experience, which can be either self-generated or acquired from other entities. Action advisin…
Student-Initiated Action Advising via Advice Novelty
Ercument Ilhan, Jeremy Gow, Diego Perez-Liebana
Action advising is a budget-constrained knowledge exchange mechanism between teacher-student peers that can help tackle exploration and sample inefficiency problems in deep reinfor…
Teaching on a Budget in Multi-Agent Deep Reinforcement Learning
Ercüment İlhan, Jeremy Gow, Diego Perez-Liebana
Deep Reinforcement Learning (RL) algorithms can solve complex sequential decision tasks successfully. However, they have a major drawback of having poor sample efficiency which can…
Semi-Automated Design Space Exploration for Formal Modelling
Gudmund Grov, Andrew Ireland, Maria Teresa Llano +3
Refinement based formal methods allow the modelling of systems through incremental steps via abstraction. Discovering the right levels of abstraction, formulating correct and meani…