Leverage the power of the Reinforcement Learning techniques to develop self-learning systems using TensorflowKey FeaturesLearn reinforcement learning concepts and their implementation using TensorFlowDiscover different problem-solving methods for Reinforcement LearningApply reinforcement learning for autonomous driving cars, robobrokers, and moreBook DescriptionReinforcement Learning (RL), allows you to develop smart, quick and self-learning systems in your business surroundings. It is an effective method to train your learning agents and solve a variety of problems in Artificial Intelligence - from games, self-driving cars and robots to enterprise applications that range from datacenter energy saving (cooling data centers) to smart warehousing solutions.The book covers the major advancements and successes achieved in deep reinforcement learning by synergizing deep neural network architectures with reinforcement learning. The book also introduces readers to the concept of Reinforcement Learning, its advantages and why it's gaining so much popularity. The book also discusses on MDPs, Monte Carlo tree searches, dynamic programming such as policy and value iteration, temporal difference learning such as Q-learning and SARSA. You will use TensorFlow and OpenAI Gym to build simple neural network models that learn from their own actions. You will also see how reinforcement learning algorithms play a role in games, image processing and NLP.By the end of this book, you will have a firm understanding of what reinforcement learning is and how to put your knowledge to practical use by leveraging the power of TensorFlow and OpenAI Gym.What you will learnImplement state-of-the-art Reinforcement Learning algorithms from the basicsDiscover various techniques of Reinforcement Learning such as MDP, Q Learning and moreLearn the applications of Reinforcement Learning in advertisement, image processing, and NLPTeach a Reinforcement Learning model to play a game using TensorFlow and the OpenAI gymUnderstand how Reinforcement Learning Applications are used in roboticsWho This Book Is ForIf you want to get started with reinforcement learning using TensorFlow in the most practical way, this book will be a useful resource. The book assumes prior knowledge of machine learning and neural network programming concepts, as well as some understanding of the TensorFlow framework. No previous experience with Reinforcement Learning is required.Table of ContentsDeep Learning -Architectures and FrameworksTraining Reinforcement Learning Agents Using OpenAI GymMarkov Decision Process (MDP)Policy GradientsQ-Learning and u0026 Deep Q NetworksAsynchronous MethodsRobo Everything - Real Strategy GamingAlphaGo - Reinforcement learning at it's BestReinforcement Learning in Autonomous DrivingFinancial Portfolio ManagementReinforcement Learning in RoboticsDeep Reinforcement Learning in AdTechReinforcement Learning in Image ProcessingDeep Reinforcement Learning in NLPAppendix 1.Further topics in Reinforcement Learning