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Foundations of Deep Reinforcement Learning: Theory and Practice in Python

by Laura Graesser

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A theory-to-implementation guide to deep reinforcement learning

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AlbakiReads Editorial

A theory-to-implementation guide to deep reinforcement learning

Deep reinforcement learning is presented here as a framework for training artificial agents on sequential decision-making problems, joining neural-network methods with reinforcement learning. The book moves from core intuition into the theory behind policy-based, value-based, and combined approaches, including REINFORCE, SARSA, DQN, Double DQN, Actor-Critic, and PPO. Implementation is a central part of the structure: examples are tied to the companion SLM Lab software library, alongside discussion of parallel training, hyperparameter tuning, benchmark results, and environment design. That combination gives the material a practical orientation while retaining attention to the concepts and algorithms underpinning deep RL.

Good fit for readers who enjoy

  • Computer science students with basic machine-learning knowledge and working Python skills
  • Software engineers seeking hands-on deep reinforcement learning implementation context
  • Learners interested in policy-based, value-based, and actor-critic algorithms

Themes

Deep reinforcement learningSequential decision-makingPython implementationReinforcement learning algorithmsEnvironment design and benchmarking

Based on publisher information and book metadata.

Book overview

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
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