AI RESEARCH
Shaping Sparse Rewards in Reinforcement Learning: A Semi-supervised Approach
arXiv CS.AI
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ArXi:2501.19128v5 Announce Type: replace-cross In many real-world scenarios, reward signal for agents are exceedingly sparse, making it challenging to learn an effective reward function for reward shaping.