4 citations · 4 across the 3 of their papers we have counts for
4 papers
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.…
Reinforcement Learning for Multi-Objective Multi-Echelon Supply Chain Optimisation
Rifny Rachman, Josh Tingey, Richard Allmendinger +2
This study develops a generalised multi-objective, multi-echelon supply chain optimisation model with non-stationary markets based on a Markov decision process, incorporating econo…
TAR: Teacher-Aligned Representations via Contrastive Learning for Quadrupedal Locomotion
Amr Mousa, Neil Karavis, Michele Caprio +2
Quadrupedal locomotion via Reinforcement Learning (RL) is commonly addressed using the teacher-student paradigm, where a privileged teacher guides a proprioceptive student policy.…
Spatial-Aware Decision-Making with Ring Attractors in Reinforcement Learning Systems
Marcos Negre Saura, Richard Allmendinger, Wei Pan +1
Ring attractors, mathematical models inspired by neural circuit dynamics, provide a biologically plausible mechanism to improve learning speed and accuracy in Reinforcement Learnin…