Deep reinforcement learning is a fast-growing discipline that is making a significant impact in
fields of autonomous vehicles robotics healthcare finance and many more. This book covers
deep reinforcement learning using deep-q learning and policy gradient models with coding
exercise. You'll begin by reviewing the Markov decision processes Bellman equations and
dynamic programming that form the core concepts and foundation of deep reinforcement learning.
Next you'll study model-free learning followed by function approximation using neural networks
and deep learning. This is followed by various deep reinforcement learning algorithms such as
deep q-networks various flavors of actor-critic methods and other policy-based methods.
You'll also look at exploration vs exploitation dilemma a key consideration in reinforcement
learning algorithms along with Monte Carlo tree search (MCTS) which played a key role inthe
success of AlphaGo. The final chapters conclude with deep reinforcement learning implementation
using popular deep learning frameworks such as TensorFlow and PyTorch. In the end you'll
understand deep reinforcement learning along with deep q networks and policy gradient models
implementation with TensorFlow PyTorch and Open AI Gym. What You'll Learn Examine deep
reinforcement learning Implement deep learning algorithms using OpenAI's Gym environment Code
your own game playing agents for Atari using actor-critic algorithms Apply best practices for
model building and algorithm training Who This Book Is For Machine learning developers and
architects who want to stay ahead of the curve in the field of AI and deep learning.
