Automating Potential-based Reward Shaping with Vision Language Model Guidance

Henrik Müller, Daniel Kudenko

Sparse rewards are inherently challenging for reinforcement learning agents as they lack intermediate feedback to guide exploration and to correctly attribute the sparse success re…