114 citations · 231 across the 17 of their papers we have counts for
9 papers · 1 filter
Automating the Search for Artificial Life with Foundation Models
Akarsh Kumar, Chris Lu, Louis Kirsch +4
With the recent Nobel Prize awarded for radical advances in protein discovery, foundation models (FMs) for exploring large combinatorial spaces promise to revolutionize many scient…
Kinetix: Investigating the Training of General Agents through Open-Ended Physics-Based Control Tasks
Michael Matthews, Michael Beukman, Chris Lu +1
While large models trained with self-supervised learning on offline datasets have shown remarkable capabilities in text and image domains, achieving the same generalisation for age…
JaxLife: An Open-Ended Agentic Simulator
Chris Lu, Michael Beukman, Michael Matthews +1
Human intelligence emerged through the process of natural selection and evolution on Earth. We investigate what it would take to re-create this process in silico. While past work h…
The AI Scientist: Towards Fully Automated Open-Ended Scientific Discovery
Chris Lu, Cong Lu, Robert Tjarko Lange +3
One of the grand challenges of artificial general intelligence is developing agents capable of conducting scientific research and discovering new knowledge. While frontier models h…
Artificial Generational Intelligence: Cultural Accumulation in Reinforcement Learning
Jonathan Cook, Chris Lu, Edward Hughes +2
Cultural accumulation drives the open-ended and diverse progress in capabilities spanning human history. It builds an expanding body of knowledge and skills by combining individual…
The Danger Of Arrogance: Welfare Equilibra As A Solution To Stackelberg Self-Play In Non-Coincidental Games
Jake Levi, Chris Lu, Timon Willi +2
The increasing prevalence of multi-agent learning systems in society necessitates understanding how to learn effective and safe policies in general-sum multi-agent environments aga…
Analysing the Sample Complexity of Opponent Shaping
Kitty Fung, Qizhen Zhang, Chris Lu +3
Learning in general-sum games often yields collectively sub-optimal results. Addressing this, opponent shaping (OS) methods actively guide the learning processes of other agents, e…
Recurrent Reinforcement Learning with Memoroids
Steven Morad, Chris Lu, Ryan Kortvelesy +3
Memory models such as Recurrent Neural Networks (RNNs) and Transformers address Partially Observable Markov Decision Processes (POMDPs) by mapping trajectories to latent Markov sta…
Learning mirror maps in policy mirror descent
Carlo Alfano, Sebastian Towers, Silvia Sapora +2
Policy Mirror Descent (PMD) is a popular framework in reinforcement learning, serving as a unifying perspective that encompasses numerous algorithms. These algorithms are derived t…