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.
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.
Table of Contents:
1 First Steps in Pytorch Reinforcement Learning
2 Exploring the Markov Decision Process with Dynamic Programming
3 Dive into Temporal Difference Methods with Deep Q Networks
4 Digging Deeper with Monte Carlo and Policy Gradient Methods
5 Going Further with Deep Deterministic Policy Gradients