<ul><li>A new method called Neuron-level Balance between Stability and Plasticity (NBSP) has been proposed for deep reinforcement learning.</li><li>NBSP focuses on the trade-off between retaining existing skills (stability) and learning new knowledge (plasticity).</li><li>The method identifies RL skill neurons crucial for knowledge retention and introduces a framework to target these neurons for preserving existing skills while enabling adaptation to new tasks.</li><li>Experimental results on Meta-World and Atari benchmarks show that NBSP outperforms existing approaches in balancing stability and plasticity.</li></ul>

Neuron-level Balance between Stability and Plasticity in Deep Reinforcement Learning

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