Deep Reinforcement Learning with Python : With PyTorch TensorFlow and OpenAI Gym Chapter 1: Introduction to Deep Reinforcement Learning Chapter 2: Markov Decision Processes Chapter 3: Model Based Algorithms Chapter 4: Model Free Approaches Chapter 5: Function Approximation Chapter 6:Deep Q Learning Chapter 7: Policy Gradient Algorithms Chapter 8: Combining Policy Gradients and Q Learning Chapter 9: Integrated Learning and Planning Chapter 10: Further Exploration and Next Steps. 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. Youll begin by reviewing the Markov decision processes Bellman equations and dynamic programming that form the core concepts and foundation of deep reinforcement learning. Next youll 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. Youll 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 in the 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 youll understand deep reinforcement learning along with deep q networks and policy gradient models implementation with TensorFlow PyTorch and Open AI Gym. You will: 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 .