Foundations of Deep Reinforcement Learning: Theory and

Foundations of Deep Reinforcement Learning: Theory and Practice in Python (Addison-Wesley Data & Analytics Series)
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 gamesâ€“such as Go, Atari games, and DotA 2â€“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
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Country USA
Brand Addison Wesley
Manufacturer Addison-Wesley Professional
Binding Paperback
PartNumber 33718478
Height 9.21258
Length 6.92912
Weight 1.19931470528
Width 0.70866
ReleaseDate 2019-12-03
NumberOfItems 1

Country	USA
Brand	Addison Wesley
Manufacturer	Addison-Wesley Professional
Binding	Paperback
PartNumber	33718478
Height	9.21258
Length	6.92912
Weight	1.19931470528
Width	0.70866
ReleaseDate	2019-12-03
NumberOfItems	1

Foundations of Deep Reinforcement Learning: Theory and Practice in Python (Addison-Wesley Data & Analytics Series)

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