Hands-on Reinforcement Learning with PyTorch [Video]

Hands-on Reinforcement Learning with PyTorch

Hands-on Reinforcement Learning with PyTorch [Video]

English | MP4 | AVC 1920×1080 | AAC 48KHz 2ch | 3h 06m | 599 MB
eLearning | Skill level: All Levels

Hands-on Reinforcement Learning with PyTorch [Video]: Dive into advanced deep reinforcement learning algorithms using PyTorch 1.x

PyTorch, 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.

  • Build key algorithms using PyTorch
  • Implement self-learning agents using PyTorch
  • Combine and modify Deep Q Networks and policy gradients to form more powerful algorithms
  • Create actor-critic and deep deterministic policy gradients, and apply proximal policy
  • Optimization in PyTorch and its extensions to improve performance
  • Explore the importance of Q learning, sample efficiency, and the on/off policy in deep reinforcement learning
  • Use function approximators, trust regions, and advanced value functions to build upon RL methods and drive new results
  • Relate the basics of RL to original Deep RL algorithms, more advanced extensions, and cutting-edge research

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.

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