Deep Reinforcement Learning Hands-On: Apply modern RL methods, with deep Q-networks, value iteration, policy gradients, TRPO, AlphaGo Zero and more

• 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 chatbots

• Understand the DL context of RL and implement complex DL models
• Learn the foundation of RL: Markov decision processes
• Evaluate RL methods including Cross-entropy, DQN, Actor-Critic, TRPO, PPO, DDPG, D4PG and others
• Discover how to deal with discrete and continuous action spaces in various environments
• Defeat Atari arcade games using the value iteration method
• Create your own OpenAI Gym environment to train a stock trading agent
• Teach your agent to play Connect4 using AlphaGo Zero
• Explore the very latest deep RL research on topics including AI-driven chatbots

Some fluency in Python is assumed. Basic deep learning (DL) approaches should be familiar to readers and some practical experience in DL will be helpful. This book is an introduction to deep reinforcement learning (RL) and requires no background in RL.