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This paper investigates the loss of plasticity in neural networks during training, particularly in deep reinforcement learning (RL) systems, which affects their adaptability to new tasks. The authors conduct empirical analyses to identify mechanisms behind plasticity loss, linking it to changes in the curvature of the loss landscape and proposing design choices to mitigate this issue. They validate their findings through experiments on larger-scale RL benchmarks, emphasizing the importance of controlling lo

Author

c3lttrong2a1btbinh

Language

EN