Links to see

https://support.mozilla.org/en-US/questions/982806
https://medium.com/mlreview/speeding-up-dqn-on-pytorch-solving-pong-in-30-minutes-81a1bd2dff55
https://jaromiru.com/2016/11/07/lets-make-a-dqn-double-learning-and-prioritized-experience-replay/
https://towardsdatascience.com/beating-video-games-with-deep-q-networks-7f73320b9592
https://medium.com/deep-math-machine-learning-ai/ch-12-1-model-free-reinforcement-learning-algorithms-monte-carlo-sarsa-q-learning-65267cb8d1b4
https://www.analyticsvidhya.com/blog/2019/04/introduction-deep-q-learning-python/
https://medium.com/@m.alzantot/deep-reinforcement-learning-demysitifed-episode-2-policy-iteration-value-iteration-and-q-978f9e89ddaa
https://medium.com/@jonathan_hui/rl-introduction-to-deep-reinforcement-learning-35c25e04c199
https://rubenfiszel.github.io/posts/rl4j/2016-08-24-Reinforcement-Learning-and-DQN.html
https://keon.io/deep-q-learning/
https://towardsdatascience.com/atari-reinforcement-learning-in-depth-part-1-ddqn-ceaa762a546f
https://pytorch.org/tutorials/intermediate/reinforcement_q_learning.html

https://pythonawesome.com/deep-reinforcement-learning-algorithms-with-pytorch/
https://www.toptal.com/deep-learning/pytorch-reinforcement-learning-tutorial

https://github.com/alamastor/elm-tetris/
https://github.com/dennybritz/reinforcement-learning
https://github.com/sourabhv/FlapPyBird
https://github.com/yenchenlin/DeepLearningFlappyBird
https://github.com/catalyst-team/catalyst

https://m.youtube.com/playlist?list=PLbm7JNvBe-MN1KIuwGk2ZeEWr5VAXdDRr