TensorFlow reinforcement learning quick start guide: get up and running with training and deploying intelligent, self-learning agents using Python

Description

Leverage the power of Tensorflow to Create powerful software agents that can self-learn to perform real-world tasks Key FeaturesExplore efficient Reinforcement Learning algorithms and code them using TensorFlow and PythonTrain Reinforcement Learning agents for problems, ranging from computer games to autonomous driving.Formulate and devise selective algorithms and techniques in your applications in no time. Book Description Advances in reinforcement learning algorithms have made it possible to use them for optimal control in several different industrial applications. With this book, you will apply Reinforcement Learning to a range of problems, from computer games to autonomous driving. The book starts by introducing you to essential Reinforcement Learning concepts such as agents, environments, rewards, and advantage functions. You will also master the distinctions between on-policy and off-policy algorithms, as well as model-free and... Table of ContentsUp and Running with Reinforcement Learning Temporal Difference, SARSA, and Q-LearningDeep Q-Network Double DQN, Dueling Architectures, and RainbowDeep Deterministic Policy GradientAsynchronous Methods - A3C and A2CTrust Region Policy Optimization and Proximal Policy OptimizationDeep RL Applied to Autonomous Driving