1 citations · 1 across the 2 of their papers we have counts for
2 papers
cs.LG2026
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…
cs.LG2025★ 1 cited
Improving the Effectiveness of Potential-Based Reward Shaping in Reinforcement Learning
Henrik Müller, Daniel Kudenko
Potential-based reward shaping is commonly used to incorporate prior knowledge of how to solve the task into reinforcement learning because it can formally guarantee policy invaria…