Everything You Need to Know About Deep Deterministic Policy Gradients (DDPG) | Tensorflow 2 Tutorial
Deep Deterministic Policy Gradients (DDPG) is an actor critic algorithm designed for use in environments with continuous action spaces. This makes it great for fields like robotics, that rely on applying continuous voltages to electric motors. You’ll get a crash course with a quick lecture, followed by a live coding tutorial.
Despite being an actor critic method, DDPG makes use of a number of innovations from deep Q learning. We’re going to make use of a replay memory for training our agent, as well as target actor and target critic networks for learning stability. One key difference is that DDPG uses a soft update rule for the target network parameters, rather than a direct hard copy of the online networks.
In this tutorial we’re going to use Tensorflow 2 to implement a deep deterministic policy gradient agent in the pendulum environment from the Open AI gym.
Learn how to turn deep reinforcement learning papers into code:
Deep Q Learning:
Actor Critic Methods:
Curiosity Driven Deep Reinforcement Learning
Natural Language Processing from First Principles:
https://www.udemy.com/course/natural-language-processing-from-first-principles/?couponCode=NLP1-OCT-21Reinforcement Learning Fundamentals
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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Code for this video is here: