← Search

Thomas D Barrett

7 accepted papers

2025

Memory-Enhanced Neural Solvers for Routing Problems

NeurIPS 2025spotlight

Routing Problems are central to many real-world applications, yet remain challenging due to their (NP-)hard nature. Amongst existing approaches, heuristics often offer the best trade-off between quality and scalability, making them suitable for industrial use. While Reinforcement Learning (RL) offer…

Cited by 0SourceScholar
2025

Metalic: Meta-Learning In-Context with Protein Language Models

ICLR 2025poster

Predicting the biophysical and functional properties of proteins is essential for in silico protein design. Machine learning has emerged as a promising technique for such prediction tasks. However, the relative scarcity of in vitro annotations means that these models often have little, or no, specif…

2024

Should we be going MAD? A Look at Multi-Agent Debate Strategies for LLMs

ICML 2024poster

Recent advancements in large language models (LLMs) underscore their potential for responding to inquiries in various domains. However, ensuring that generative agents provide accurate and reliable answers remains an ongoing challenge. In this context, multi-agent debate (MAD) has emerged as a promi…

2023

Combinatorial Optimization with Policy Adaptation using Latent Space Search

NeurIPS 2023poster

Combinatorial Optimization underpins many real-world applications and yet, designing performant algorithms to solve these complex, typically NP-hard, problems remains a significant research challenge. Reinforcement Learning (RL) provides a versatile framework for designing heuristics across a broad…

2023

Reinforcement Learning for Branch-and-Bound Optimisation Using Retrospective Trajectories

AAAI 2023technical

Combinatorial optimisation problems framed as mixed integer linear programmes (MILPs) are ubiquitous across a range of real-world applications. The canonical branch-and-bound algorithm seeks to exactly solve MILPs by constructing a search tree of increasingly constrained sub-problems. In practice, i…

2023

Winner Takes It All: Training Performant RL Populations for Combinatorial Optimization

NeurIPS 2023poster

Applying reinforcement learning (RL) to combinatorial optimization problems is attractive as it removes the need for expert knowledge or pre-solved instances. However, it is unrealistic to expect an agent to solve these (often NP-)hard problems in a single shot at inference due to their inherent com…

Cited by 36SourcePDFScholar
2022

Universally Expressive Communication in Multi-Agent Reinforcement Learning

NeurIPS 2022accept

Allowing agents to share information through communication is crucial for solving complex tasks in multi-agent reinforcement learning. In this work, we consider the question of whether a given communication protocol can express an arbitrary policy. By observing that many existing protocols can be vi…