4 papers
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…
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…