The Rank and Gradient Lost in Non-stationarity: Sample Weight Decay for Mitigating Plasticity Loss in Reinforcement Learning

The Rank and Gradient Lost in Non-stationarity: Sample Weight Decay for Mitigating Plasticity Loss in Reinforcement Learning explores A theoretical framework and lightweight method to restore gradient magnitude in deep reinforcement learning, improving adaptation to new data and achieving state-of-the-art performance.. Commercial viability score: 7/10 in Reinforcement Learning.