Deep Reinforcement Learning with Python - Nimish Sanghi Kartoniert (TB)

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 v arious 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.