Notes on “Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor”

I am reading this paper (https://arxiv.org/abs/1801.01290) and wanted to take down some notes about it. Introduction Soft Actor-Critic is a special version of Actor-Critic algorithms. Actor-Critic algorithms are one kind of policy gradient methods. Policy gradient methods are different than value-based methods (like Q-learning), where you learn Q-values and then infer the best action to …

Euler’s Formula and Fourier Transform

Euler’s formula states that $latex e^{ix} =\cos{x}+ i \sin{x}$. When $latex x = \pi$, the formula becomes $latex e^{\pi} = -1$ known as Euler’s identity. An easy derivation of Euler’s formula is given in [3] and [5]. According to Maclaurin series (a special case of taylor expansion $latex f(x)=f(a)+f'(a)(x-a)+\frac{f”(a)}{2!}(x-a)^2+\cdots$ when $latex a=0$),  $latex e^x=1+x+\frac{x^2}{2!}+\frac{x^3}{3!}+\frac{x^4}{4!}+\cdots &s=2$ …