This work studies static sparsity as a way to scale deep reinforcement learning. A single random pruning step before training improves parameter efficiency and helps networks resist plasticity loss and gradient interference. The evaluation includes visual and streaming RL settings. Read the conference record.
Guozheng Ma and Lu Li contributed equally.