Deep Reinforcement Learning Hands-On: Apply modern RL methods, with deep Q-networks, value iteration, policy gradients, TRPO, AlphaGo Zero and more
by Maxim Lapan
Deep Reinforcement Learning Hands-On explores practical methods for applying modern reinforcement learning. Maxim Lapan covers deep Q-networks, value iteration, policy gradients, TRPO, AlphaGo Zero, and more, offering readers a focused guide to important techniques in deep reinforcement learning.
About This Book
Deep Reinforcement Learning Hands-On introduces practical approaches to modern reinforcement learning.
The book covers deep Q-networks, value iteration, policy gradients, and trust region policy optimization.
It also examines AlphaGo Zero and other techniques within the broader field of deep reinforcement learning.
Written by Maxim Lapan, this resource is focused on applying reinforcement learning methods through hands-on study.
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