16 citations · 25 across the 10 of their papers we have counts for
6 papers · 2 filters
NAVIX: Scaling MiniGrid Environments with JAX
Eduardo Pignatelli, Jarek Liesen, Robert Tjarko Lange +3
As Deep Reinforcement Learning (Deep RL) research moves towards solving large-scale worlds, efficient environment simulations become crucial for rapid experimentation. However, mos…
Can Learned Optimization Make Reinforcement Learning Less Difficult?
Alexander David Goldie, Chris Lu, Matthew Thomas Jackson +2
While reinforcement learning (RL) holds great potential for decision making in the real world, it suffers from a number of unique difficulties which often need specific considerati…
Behaviour Distillation
Andrei Lupu, Chris Lu, Jarek Liesen +2
Dataset distillation aims to condense large datasets into a small number of synthetic examples that can be used as drop-in replacements when training new models. It has application…
Discovering Minimal Reinforcement Learning Environments
Jarek Liesen, Chris Lu, Andrei Lupu +3
Reinforcement learning (RL) agents are commonly trained and evaluated in the same environment. In contrast, humans often train in a specialized environment before being evaluated,…
Discovering Preference Optimization Algorithms with and for Large Language Models
Chris Lu, Samuel Holt, Claudio Fanconi +4
Offline preference optimization is a key method for enhancing and controlling the quality of Large Language Model (LLM) outputs. Typically, preference optimization is approached as…
Discovering Temporally-Aware Reinforcement Learning Algorithms
Matthew Thomas Jackson, Chris Lu, Louis Kirsch +3
Recent advancements in meta-learning have enabled the automatic discovery of novel reinforcement learning algorithms parameterized by surrogate objective functions. To improve upon…