Deep reinforcement learning hands-on : apply modern RL methods, with deep Q-networks, value iteration, policy gradients, TRPO, AlphaGo Zero and more / Maxim Lapan

Includes index.

What is Reinforcement Learning? -- OpenAI GymDeep Learning with PyTorch -- The Cross-Entropy Method -- Tabular Learning and the Bellman Equation -- Deep Q-Networks -- DQN Extensions -- Stocks Trading Using RL -- Policy Gradients - An Alternative -- The Actor-Critic Method -- Asynchronous Advantage Actor-Critic --Chatbots Training with RL Web Navigation -- Continuous Action Space -- Trust Regions - TRPO, PPO, and ACKTR -- Black-Box Optimization in RL -- Beyond Model-Free - Imagination -- AlphaGo Zero

Deep Reinforcement Learning Hands-On is a comprehensive guide to the very latest DL tools and their limitations. You will evaluate methods including Cross-entropy and policy gradients, before applying them to real-world environments. Take on both the Atari set of virtual games and family favorites such as Connect4. The book provides an introduction to the basics of RL, giving you the know-how to code intelligent learning agents to take on a formidable array of practical tasks. Discover how to implement Q-learning on 'grid world' environments, teach your agent to buy and trade stocks, and find out how natural language models are driving the boom in chatbots.