In reinforcement learning, scaling batch size alone is ineffective when using small networks that cannot capture complex patterns; however, when deep networks with sufficient capacity are successfully trained, scaling network capacity unlocks an additional dimension of effectiveness for batch size scaling, enabling more efficient training.
Deep Dive
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Scaling Batch Size: Deep Networks Unlock New RL PotentialHinzugefügt:
We actually find that you know, perhaps one hypothesis might be like perhaps the reason why scaling batches isn't that effective in traditional RL because like we've been using these tiny networks that haven't been able to capture that. And one of our experiments is that like because we are enabled successful training of deep network, we actually were able to this is a great test bed for you know, like testing this hypothesis and we find that indeed as we scale the network capacity, we also unlock this different dimension of scaling batch size.
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