  In this work we identify the dormant neuron phenomenon in deep reinforcement
learning, where an agent's network suffers from an increasing number of
inactive neurons, thereby affecting network expressivity. We demonstrate the
presence of this phenomenon across a variety of algorithms and environments,
and highlight its effect on learning. To address this issue, we propose a
simple and effective method (ReDo) that Recycles Dormant neurons throughout
training. Our experiments demonstrate that ReDo maintains the expressive power
of networks by reducing the number of dormant neurons and results in improved
performance.
