ОСНОВЫ ГЛУБОКОГО ОБУЧЕНИЯ ГРЕССА

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Характеристики

Артикул
10872810398
Состояние
Новый
Język publikacji
angielski
Tytuł
DEEP REINFORCEMENT LEARNING IN PYTHON: A HANDS-ON INTRODUCTION
Autor
Laura Harding Graesser
Nośnik
książka papierowa
Okładka
miękka
Rok wydania
2020
Wydawnictwo
inne
Liczba stron
360

Описание

Foundations of Deep Reinforcement Learning: Theory and Practice in Python

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
FOUNDATIONS OF DEEP REINFORCEMENT LEARNING GRAESSE
FOUNDATIONS OF DEEP REINFORCEMENT LEARNING GRAESSE

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