7 papers
MEMENTO: Memory-Guided Memetic Code-as-Policy Evolution
Alkis Sygkounas, Victor Aregbede, Amy Loutfi +1
Long-horizon embodied tasks require policies that execute many dependent actions before task success can be observed. Representing policies as executable control pro- grams (code-a…
Measuring Learning Progress via Gradient-Momentum Coupling
Samuel Blad, Martin Längkvist, Amy Loutfi
Measuring learning progress is essential for curiosity-driven exploration in reinforcement learning, but widely used signals such as prediction error often fail to distinguish mean…
Evolutionary Discovery of Reinforcement Learning Algorithms via Large Language Models
Alkis Sygkounas, Amy Loutfi, Andreas Persson
Reinforcement learning algorithms are defined by their learning update rules, which are typically hand-designed and fixed. We present an evolutionary framework for discovering rein…
COvolve: Adversarial Co-Evolution of Large-Language-Model-Generated Policies and Environments via Two-Player Zero-Sum Game
Alkis Sygkounas, Rishi Hazra, Andreas Persson +2
A central challenge in building continually improving agents is that training environments are typically static or manually constructed. This restricts continual learning and gener…
LAP: A Language-Aware Planning Model For Procedure Planning In Instructional Videos
Lei Shi, Victor Aregbede, Andreas Persson +3
Procedure planning requires a model to predict a sequence of actions that transform a start visual observation into a goal in instructional videos. While most existing methods rely…
REvolve: Reward Evolution with Large Language Models using Human Feedback
Rishi Hazra, Alkis Sygkounas, Andreas Persson +2
Designing effective reward functions is crucial to training reinforcement learning (RL) algorithms. However, this design is non-trivial, even for domain experts, due to the subject…