MP4 | Video: AVC 1920×1080 30fps | Audio: AAC 48KHz 2ch | Duration: 4hGenre: eLearning | Language: English | Size: 593 MBLearnBuild key algorithms using PyTorchImplement self-learning agents using PyTorchCombine and modify Deep Q Networks and policy gradients to form more powerful algorithmsCreate actor-critic and deep deterministic policy gradients, and apply proximal policyOptimization in PyTorch and its extensions to improve performanceExplore the importance of Q learning, sample efficiency, and the on/off policy in deep reinforcement learningUse function approximators, trust regions, and advanced value functions to build upon RL methods and drive new resultsRelate the basics of RL to original Deep RL algorithms, more advanced extensions, and cutting-edge researchAboutPyTorch, Facebook’s deep learning framework, is clear, easy to code and easy to debug, thus providing a straightforward and simple experience for developers.

This course is your hands-on guide to the core concepts of deep reinforcement learning and its implementation in PyTorch.

We will help you get your PyTorch environment ready before moving on to the core concepts that encompass deep reinforcement learning.

Following a practical approach, you will build reinforcement learning algorithms and develop/train agents in simulated OpenAI Gym environments.

You’ll learn the skills you need to implement deep reinforcement learning concepts so you can get started building smart systems that learn from their own experiences.

By the end of this course, you will have enhanced your knowledge of deep reinforcement learning algorithms and will be confident enough to effectively use PyTorch to build your RL projects.

The code bundle for this course is available at:FeaturesAn ideal blend of conceptual explanations and deep RL implementation in PyTorchGo from zero to hero by exploring extensions and current researchCovers deep Q networks, policy gradients, actor-critic, deep deterministic policy gradients, and proximal policy optimization