Sách Foundations of Deep Reinforcement Learning Theory and Practice in Python

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Sách Foundations of Deep Reinforcement Learning Theory and Practice in Python

Sách Foundations of Deep Reinforcement Learning Theory and Practice in Python

Sách Foundations of Deep Reinforcement Learning Theory and Practice in Python

Sách Foundations of Deep Reinforcement Learning Theory and Practice in Python

Sách Foundations of Deep Reinforcement Learning Theory and Practice in Python

Sách Foundations of Deep Reinforcement Learning Theory and Practice in Python

Sách Foundations of Deep Reinforcement Learning Theory and Practice in Python

Sách Foundations of Deep Reinforcement Learning Theory and Practice in Python

Sách Foundations of Deep Reinforcement Learning Theory and Practice in Python

Sách Foundations of Deep Reinforcement Learning Theory and Practice in Python

Sách Foundations of Deep Reinforcement Learning Theory and Practice in Python

Sách Foundations of Deep Reinforcement Learning Theory and Practice in Python

Sách Foundations of Deep Reinforcement Learning Theory and Practice in Python

Sách Foundations of Deep Reinforcement Learning Theory and Practice in Python

Sách Foundations of Deep Reinforcement Learning Theory and Practice in Python

Sách Foundations of Deep Reinforcement Learning Theory and Practice in Python

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Sách Foundations of Deep Reinforcement Learning Theory and Practice in Python

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Sách Foundations of Deep Reinforcement Learning Theory and Practice in Python

Sách Foundations of Deep Reinforcement Learning Theory and Practice in Python (sách keo gáy, bìa mềm)

Categories:Computers - Computer Science

Year:2020

Publisher:Addison-Wesley

Language:english

Pages: 413

The Contemporary Introduction to Deep Reinforcement Learning that Combines Theory and Practice

Deep

reinforcement learning (deep RL) combines deep learning and

reinforcement learning, in which artificial agents learn to solve

sequential decision-making problems. In the past decade deep RL has

achieved remarkable results on a range of problems, from single and

multiplayer games–such as Go, Atari games, and DotA 2–to robotics.

Foundations of Deep Reinforcement Learning

is an introduction to deep RL that uniquely combines both theory and

implementation. It starts with intuition, then carefully explains the

theory of deep RL algorithms, discusses implementations in its companion

software library SLM Lab, and finishes with the practical details of

getting deep RL to work.

This guide is ideal for both computer

science students and software engineers who are familiar with basic

machine learning concepts and have a working understanding of Python.

Understand each key aspect of a deep RL problem

Explore policy- and value-based algorithms, including REINFORCE, SARSA, DQN, Double DQN, and Prioritized Experience Replay (PER)

Delve into combined algorithms, including Actor-Critic and Proximal Policy Optimization (PPO)

Understand how algorithms can be parallelized synchronously and asynchronously

Run algorithms in SLM Lab and learn the practical implementation details for getting deep RL to work

Explore algorithm benchmark results with tuned hyperparameters

Understand how deep RL environments are designed

Register

your book for convenient access to downloads, updates, and/or

corrections as they become available. See inside book for details.

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