Why Deep Q Learning Needs A Target Network and Replay Memory | Course Excerpt For Cyber Monday

The two biggest innovations in deep Q learning were the introduction of the target network and the replay memory. One would think that simply bolting a deep neural network to the Q learning algorithm would be enough for a robust deep Q learning agent, but that isn't the case. In this video I'll show you how this naive implementation of the deep q learning agent fails, and spectacularly at that.

#DeepQLearning #PyTorch #ReinforcementLearning

This is an excerpt from my new course, Actor Critic Methods from Paper to Code

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=NLP-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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Видео Why Deep Q Learning Needs A Target Network and Replay Memory | Course Excerpt For Cyber Monday канала Machine Learning with Phil