<ul><li>Deep reinforcement learning (RL) agents face issues with neuronal activity loss affecting their ability to adapt to new data.</li><li>Current methods like the tau-dormant neuron ratio may lose statistical power in complex architectures.</li><li>A new metric called GraMa (Gradient Magnitude Neural Activity Metric) focuses on gradients instead of activations to quantify neuron-level learning capacity.</li><li>Resetting neurons based on GraMa (ReGraMa) has shown consistent improvements in learning performance across various deep RL algorithms and benchmarks.</li></ul>

Measure gradients, not activations! Enhancing neuronal activity in deep reinforcement learning

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