4 citations · 4 across the 10 of their papers we have counts for
5 papers · 1 filter
PROMO: Preference-conditioned Multi-Objective Reinforcement Learning for Quadrupedal Robots
Amr Mousa, Rifny Rachman, Neil Karavis +2
Quadrupedal locomotion requires balancing conflicting objectives such as command tracking, stability, and energy efficiency, yet conventional reinforcement learning (RL) hardcodes…
Meta-Reinforcement Learning via Evolution for Multi-Objective Combinatorial Supply Chain Optimisation
Rifny Rachman, Bahrul Ilmi Nasution, Josh Tingey +3
Meta-reinforcement learning is a promising approach to multi-objective optimisation because it enables rapid policy adaptation across changing environments and preference settings.…
Accelerating Multi-Objective Bayesian Optimisation via Predictive-Gradient Catalysts
Alma Rahat, Tinkle Chugh, Jonathan Fieldsend +1
This paper presents a general acceleration mechanism for multi-objective Bayesian optimisation (MOBO) that leverages Gaussian process predictive gradients as auxiliary signals. Rat…
MIRACL: A Diverse Meta-Reinforcement Learning for Multi-Objective Multi-Echelon Combinatorial Supply Chain Optimisation
Rifny Rachman, Josh Tingey, Richard Allmendinger +3
Multi-objective reinforcement learning (MORL) is effective for multi-echelon combinatorial supply chain optimisation, where tasks involve high dimensionality, uncertainty, and comp…
Bayesian Generative Adversarial Networks via Gaussian Approximation for Tabular Data Synthesis
Bahrul Ilmi Nasution, Mark Elliot, Richard Allmendinger
Generative Adversarial Networks (GAN) have been used in many studies to synthesise mixed tabular data. Conditional tabular GAN (CTGAN) have been the most popular variant but strugg…