I've been reading some papers on reinforcement learning.

$$\Delta w=\frac{\partial ln\ \pi_w}{\partial w}r$$

I often see expressions, similar to the above one, where the weights (denoted by $w$) are updated following the partial derivative of the policy function (denoted by $\pi_w$) with respect to its weights.

But why do we take the $\ln$ or $\log$? What is its purpose?

I've been reading some papers on reinforcement learning.

$$\Delta w=\frac{\partial ln\ p_w}{\partial w}r$$

I often see expressions, similar to the above one, where the weights (denoted by $w$) are updated following the partial derivative of the policy function (denoted by $p_w$) with respect to its weights.

But why do we take the $\log$? What is its purpose?

$$\Delta w=\frac{\partial ln\ p_w}{\partial w}r$$

I've been studying recently reading some papers on reinforcement learning. I often see expressions similar to above where the weights are updated following the partial derivative of the policy function with respect to its weights. My questions is on the $ln$ or $log$, which is always there, and what is its purpose?

I've been reading some papers on reinforcement learning.

$$\Delta w=\frac{\partial ln\ \pi_w}{\partial w}r$$

I often see expressions, similar to the above one, where the weights (denoted by $w$) are updated following the partial derivative of the policy function (denoted by $\pi_w$) with respect to its weights.

But why do we take the $\ln$ or $\log$? What is its purpose?