Foundations of Deep Reinforcement Learning : Theory and Practice in Python
Book Details
Format
Paperback / Softback
Book Series
Addison-Wesley Data & Analytics Series
ISBN-10
0135172381
ISBN-13
9780135172384
Publisher
Pearson Education (US)
Imprint
Addison Wesley
Country of Manufacture
US
Country of Publication
GB
Publication Date
Feb 11th, 2020
Print length
416 Pages
Weight
618 grams
Dimensions
23.10 x 16.80 x 1.30 cms
Product Classification:
DatabasesArtificial intelligence
Ksh 6,850.00
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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.
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
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In just a few years, deep reinforcement learning (DRL) systems such as DeepMinds DQN have yielded remarkable results. This hybrid approach to machine learning shares many similarities with human learning: its unsupervised self-learning, self-discovery of strategies, usage of memory, balance of exploration and exploitation, and its exceptional flexibility. Exciting in its own right, DRL may presage even more remarkable advances in general artificial intelligence.
Deep Reinforcement Learning in Python: A Hands-On Introduction is the fastest and most accessible way to get started with DRL. The authors teach through practical hands-on examples presented with their advanced OpenAI Lab framework. While providing a solid theoretical overview, they emphasize building intuition for the theory, rather than a deep mathematical treatment of results. Coverage includes:
- Components of an RL system, including environment and agents
- Value-based algorithms: SARSA, Q-learning and extensions, offline learning
- Policy-based algorithms: REINFORCE and extensions; comparisons with value-based techniques
- Combined methods: Actor-Critic and extensions; scalability through async methods
- Agent evaluation
- Advanced and experimental techniques, and more
- How to achieve breakthrough machine learning performance by combining deep neural networks with reinforcement learning
- Reduces the learning curve by relying on the authors’ OpenAI Lab framework: requires less upfront theory, math, and programming expertise
- Provides well-designed, modularized, and tested code examples with complete experimental data sets to illuminate the underlying algorithms
- Includes case studies, practical tips, definitions, and other aids to learning and mastery
- Prepares readers for exciting future advances in artificial general intelligence
The accessible, hands-on, full-color tutorial for building practical deep reinforcement learning solutions
- How to achieve breakthrough machine learning performance by combining deep neural networks with reinforcement learning
- Reduces the learning curve by relying on the authors’ OpenAI Lab framework: requires less upfront theory, math, and programming expertise
- Provides well-designed, modularized, and tested code examples with complete experimental data sets to illuminate the underlying algorithms
- Includes case studies, practical tips, definitions, and other aids to learning and mastery
- Prepares readers for exciting future advances in artificial general intelligence
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