4 citations · 6 across the 5 of their papers we have counts for
5 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…
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…
Interactive Double Deep Q-network: Integrating Human Interventions and Evaluative Predictions in Reinforcement Learning of Autonomous Driving
Alkis Sygkounas, Ioannis Athanasiadis, Andreas Persson +2
Integrating human expertise with machine learning is crucial for applications demanding high accuracy and safety, such as autonomous driving. This study introduces Interactive Doub…
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…