1 citations · 1 across the 4 of their papers we have counts for
5 papers
Calibrated Partial Resets: Preventing Policy Collapse in Continual Reinforcement Learning
Luc McCutcheon, Evangelos Chatzaroulas, Saber Fallah
Neural networks are hindered by accumulating dormant neurons and loss of expressivity throughout training, particularly in non-stationary data settings, such as continual supervise…
Meta-World+: An Improved, Standardized, RL Benchmark
Reginald McLean, Evangelos Chatzaroulas, Luc McCutcheon +9
Meta-World is widely used for evaluating multi-task and meta-reinforcement learning agents, which are challenged to master diverse skills simultaneously. Since its introduction how…
Multi-Task Reinforcement Learning Enables Parameter Scaling
Reginald McLean, Evangelos Chatzaroulas, Jordan Terry +3
Multi-task reinforcement learning (MTRL) aims to endow a single agent with the ability to perform well on multiple tasks. Recent works have focused on developing novel sophisticate…
In-Context Ensemble Learning from Pseudo Labels Improves Video-Language Models for Low-Level Workflow Understanding
Moucheng Xu, Evangelos Chatzaroulas, Luc McCutcheon +4
A Standard Operating Procedure (SOP) defines a low-level, step-by-step written guide for a business software workflow. SOP generation is a crucial step towards automating end-to-en…
Deep Reinforcement Learning for Stabilization of Large-scale Probabilistic Boolean Networks
Sotiris Moschoyiannis, Evangelos Chatzaroulas, Vytenis Sliogeris +1
The ability to direct a Probabilistic Boolean Network (PBN) to a desired state is important to applications such as targeted therapeutics in cancer biology. Reinforcement Learning…