This practical guide will teach you how deep learning (DL) can be used to solve complex real-world problems. Key Features * Explore deep reinforcement learning (RL), from the first principles to the latest algorithms * Evaluate high-profile RL methods, including value iteration, deep Q-networks, policy gradients, TRPO, PPO, DDPG, D4PG, evolution strategies and genetic algorithms * Keep up with the very latest industry developments, including AI-driven chatbotsBook DescriptionRecent developments in reinforcement learning (RL), combined with deep learning (DL), have seen unprecedented progress made towards training agents to solve complex problems in a human-like way. Google’s use of algorithms to play and defeat the well-known Atari arcade games has propelled the field to prominence, and researchers are generating new ideas at a rapid pace. Deep Reinforcement Learning Hands-On is a comprehensive guide to the very latest DL tools and their limitations. You will evaluate methods
