links.txt

http://rail.eecs.berkeley.edu/deeprlcourse/
https://lilianweng.github.io/lil-log/2018/04/08/policy-gradient-algorithms.html#reinforce
https://towardsdatascience.com/policy-gradient-methods-104c783251e0
https://github.com/andri27-ts/Reinforcement-Learning/blob/master/Week4/PolicyGradient.ipynb
https://www.youtube.com/watch?v=Tol_jw5hWnI&list=PLkFD6_40KJIxJMR-j5A1mkxK26gh_qg37&index=5https://www.youtube.com/watch?v=2pWv7GOvuf0&list=PLqYmG7hTraZDM-OYHWgPebj2MfCFzFObQ

Entropy: https://towardsdatascience.com/entropy-regularization-in-reinforcement-learning-a6fa6d7598df

A2C github: https://github.com/MG2033/A2C
https://kargarisaac.medium.com/rl-series-a2c-and-a3c-in-pytorch-6e9edf5c8788
https://www.reddit.com/r/reinforcementlearning/comments/fz79hw/r_a3c_vs_a2c_did_i_get_this_right/
http://rail.eecs.berkeley.edu/deeprlcourse-fa17/f17docs/lecture_5_actor_critic_pdf

A3C
https://github.com/ikostrikov/pytorch-a3c

SAC
https://github.com/thomashirtz/soft-actor-critic