4 papers
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.…
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…
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…