DeepMind Makes Prototyping Papers Easy with ACME
DeepMind’s ACME framework makes implementing deep reinforcement learning agents incredibly easy. By using a modularized approach to agent design, agents can be scaled from a single thread up to hundreds easily. In this video I’ll give you a brief overview of how all the pieces fit together.
Learn how to turn deep reinforcement learning papers into code:
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Deep Q Learning:
https://www.udemy.com/course/deep-q-learning-from-paper-to-code/?couponCode=DQN-JUNE-22
Actor Critic Methods:
https://www.udemy.com/course/actor-critic-methods-from-paper-to-code-with-pytorch/?couponCode=AC-JUNE-22
Curiosity Driven Deep Reinforcement Learning
https://www.udemy.com/course/curiosity-driven-deep-reinforcement-learning/?couponCode=ICM-JUNE-22
Natural Language Processing from First Principles:
https://www.udemy.com/course/natural-language-processing-from-first-principles/?couponCode=NLP1-JUNE-22
Reinforcement Learning Fundamentals
https://www.manning.com/livevideo/reinforcement-learning-in-motion
Here are some books / courses I recommend (affiliate links):
Grokking Deep Learning in Motion: https://bit.ly/3fXHy8W
Grokking Deep Learning: https://bit.ly/3yJ14gT
Grokking Deep Reinforcement Learning: https://bit.ly/2VNAXql
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