Deep Reinforcement Learning Hands-On

With Deep Reinforcement Learning Hands-On, 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. (Limited-time offer)

Book Description

What is Reinforcement Learning? • OpenAI Gym • Deep 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

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