The minibatch size in a neural network training process refers to the number of samples used in each iteration to update the weights of the network. A larger minibatch size can lead to slower weight updates and potentially slower convergence, while a smaller minibatch size can result in faster weight updates but with more noisy updates.
In your case, it seems that a larger minibatch size is causing the action signal to saturate earlier. This could be because with a larger minibatch size, the weight updates are slower, and therefore the network takes longer to adapt to the changing environment. As a result, the action signal reaches its limits ([-0.15, 0.15]) earlier. It may be worth experimenting with different minibatch sizes to find the optimal value for your specific case.
