Welcome to my new reinforcement learning course! For the next 10 weeks we're going to go from the basics to the state of the art in this popular subfield of machine learning using video game environments as our testbed. RL is a huge reason DeepMind and OpenAI have been so successful thus far in creating world changing AI bots. Make sure to subscribe so you'll get updated with every new video I release. And don't worry if you don't understand policy iteration or value iteration just yet, I merely wanted to introduce these phrases in this video, next week i'm going to really dive into what these 2 methods look like programmatically.
Code for this video (with coding challenge):
https://github.com/llSourcell/AI_for_video_games_demo
Syllabus for this course:
https://github.com/llSourcell/AI_for_Video_Games_Syllabus
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More learning resources:
https://medium.com/emergent-future/simple-reinforcement-learning-with-tensorflow-part-0-q-learning-with-tables-and-neural-networks-d195264329d0
http://icml.cc/2016/tutorials/deep_rl_tutorial.pdf
https://github.com/MorvanZhou/Reinforcement-learning-with-tensorflow
https://www.analyticsvidhya.com/blog/2017/01/introduction-to-reinforcement-learning-implementation/
https://web.mst.edu/~gosavia/tutorial.pdf
http://karpathy.github.io/2016/05/31/rl/
http://www.wildml.com/2016/10/learning-reinforcement-learning/
https://www.quora.com/What-are-some-good-tutorials-on-reinforcement-learning
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