From Sparse to Dense: Toddler–inspired Reward Transition in Goal–Oriented Reinforcement Learning
Abstract: Reinforcement learning (RL) agents face fundamental challenges in balancing exploration and exploitation, particularly when sparse or dense rewards bias learning toward sub–optimal behaviors ...
Abstract: Deepfake detection remains a pressing challenge due to the rapid evolution of forgery techniques and the demand for robust, generalizable, and interpretable solutions. We present a sparse ...
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