Artificial Intelligence Learns to Walk with Actor Critic Deep Reinforcement Learning | TD3 Tutorial
Twin Delayed Deep Deterministic Policy Gradients (TD3) is a state of the art actor critic algorithm for mastering environments with continuous action spaces. It's based on the deep deterministic policy gradients algorithm, but deals with the problem of overestimation bias that arises from the use of deep neural networks as function approximators. This is one of my favorite deep reinforcement learning algorithms, and we're going to use it on the Bipedal Walker environment from the Open ai gym in this interactive tensorflow 2 coding tutorial. You can find the code for this tutorial here: https://github.com/philtabor/Youtube-... Learn how to turn deep reinforcement learning papers into code: Get instant access to all my courses, including the new Prioritized Experience Replay course, with my subscription service. $29 a month gives you instant access to 42 hours of instructional content plus access to future updates, added monthly. Discounts available for Udemy students (enrolled longer than 30 days). Just send an email to [email protected] https://www.neuralnet.ai/courses Or, pickup my Udemy courses here: Deep Q Learning: https://www.udemy.com/course/deep-q-l... Actor Critic Methods: https://www.udemy.com/course/actor-cr... Curiosity Driven Deep Reinforcement Learning https://www.udemy.com/course/curiosit... Natural Language Processing from First Principles: https://www.udemy.com/course/natural-... Reinforcement Learning Fundamentals https://www.manning.com/livevideo/rei... 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 Come hang out on Discord here: / discord Need personalized tutoring? Help on a programming project? Shoot me an email! [email protected] Website: https://www.neuralnet.ai Github: https://github.com/philtabor Twitter: / mlwithphil
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