3 citations · 7 across the 6 of their papers we have counts for
5 papers · 1 filter
Combinatorial Optimization with Policy Adaptation using Latent Space Search
Felix Chalumeau, Shikha Surana, Clement Bonnet +4
Combinatorial Optimization underpins many real-world applications and yet, designing performant algorithms to solve these complex, typically NP-hard, problems remains a significant…
Debiasing Meta-Gradient Reinforcement Learning by Learning the Outer Value Function
Clément Bonnet, Laurence Midgley, Alexandre Laterre
Meta-gradient Reinforcement Learning (RL) allows agents to self-tune their hyper-parameters in an online fashion during training. In this paper, we identify a bias in the meta-grad…
Learning to Solve Combinatorial Graph Partitioning Problems via Efficient Exploration
Thomas D. Barrett, Christopher W. F. Parsonson, Alexandre Laterre
From logistics to the natural sciences, combinatorial optimisation on graphs underpins numerous real-world applications. Reinforcement learning (RL) has shown particular promise in…
Reinforcement Learning for Branch-and-Bound Optimisation using Retrospective Trajectories
Christopher W. F. Parsonson, Alexandre Laterre, Thomas D. Barrett
Combinatorial optimisation problems framed as mixed integer linear programmes (MILPs) are ubiquitous across a range of real-world applications. The canonical branch-and-bound algor…
One Step at a Time: Pros and Cons of Multi-Step Meta-Gradient Reinforcement Learning
Clément Bonnet, Paul Caron, Thomas Barrett +2
Self-tuning algorithms that adapt the learning process online encourage more effective and robust learning. Among all the methods available, meta-gradients have emerged as a promis…