Foundations of Deep Reinforcement Learning: Theory and Practice in Python – PDF

The Present-day Introduction to Deep Reinforcement Learning that Combines Theory and Practice Deep reinforcement studying (deep RL) integrates deep studying and reinforcement studying, in which synthetic brokers study to resolve sequential choice-making issues. In the final decade deep RL has attained exceptional outcomes on a spread of issues, from single and multiplayer video games—comparable to Atari video games, Go and DotA 2—to robotics. Foundations of Deep Reinforcement Learning (PDF) is an introduction to deep RL that uniquely integrates each concept and implementation. It begins with instinct, then meticulously explains the speculation of deep RL algorithms, discusses implementations in its companion software program library SLM Lab, and ends with the sensible particulars of getting deep RL to work. This information is ideal for each laptop science college students and software program engineers who’re conversant in elementary machine studying ideas and have a working understanding of Python.

• Understand each key side of a deep RL downside
• Understand how deep RL environments are designed
• Explore algorithm benchmark outcomes with tuned hyperparameters
• Understand how algorithms could be parallelized synchronously and asynchronously
• Delve into mixed algorithms, together with Actor-Critic and Proximal Policy Optimization (PPO)
• Run algorithms in SLM Lab and study the sensible implementation particulars for getting deep RL to work
• Explore policy- and worth-primarily based algorithms, together with REINFORCE, SARSA, DQN, Double DQN, and Prioritized Experience Replay (PER)

Reviews

“This ebook gives an accessible introduction to deep reinforcement studying encompassing the mathematical ideas behind common algorithms together with their sensible implementation. I believe the ebook will likely be a invaluable useful resource for anybody seeking to implement deep reinforcement studying in apply.” — Volodymyr Mnih, lead developer of DQN “An wonderful e-book to rapidly construct experience in the speculation, language, and sensible implementation of deep reinforcement studying algorithms. A transparent exposition which makes use of acquainted notation; all the newest methods defined with concise, readable code, and not a web page wasted in unrelated detours: it’s the superb option to develop a strong basis on the subject.” — Vincent Vanhoucke, principal scientist, Google NOTE: The product only contains the ebook Foundations of Deep Reinforcement Learning: Theory and Practice in Python in PDF. No access codes are included.