Скачать с ютуб Reinforcement Learning in Continuous Action Spaces | DDPG Tutorial (Pytorch) в хорошем качестве

Reinforcement Learning in Continuous Action Spaces | DDPG Tutorial (Pytorch)

In this tutorial we will code a deep deterministic policy gradient (DDPG) agent in Pytorch, to beat the continuous lunar lander environment. DDPG combines the best of Deep Q Learning and Actor Critic Methods into an algorithm that can solve environments with continuous action spaces. We will have an actor network that learns the (deterministic) policy, coupled with a critic network to learn the action-value functions. We will make use of a replay buffer to maximize sample efficiency, as well as target networks to assist in algorithm convergence and stability. To deal with the explore exploit dilemma, we will introduce noise into the agent's action choice function. This noise is the Ornstein Uhlenbeck noise that models temporal correlations of brownian motion. Keep in mind that the performance you see is from an agent that is still in training mode, i.e. it still has some noise in its action. A fully trained agent in evaluation mode will perform even better. You can fix this up in the code by adding a parameter to the choose action function, and omitting the noise if you pass in a variable to indicate you are in evaluation mode. #DeepDeterministicPolicyGradients #DDPG #ContinuousLunarLander 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