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.

• 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 parallelised 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

The full text downloaded to your computer

With eBooks you can:

• search for key concepts, words and phrases
• make highlights and notes as you study
• share your notes with friends

eBooks are downloaded to your computer and accessible either offline through the Bookshelf (available as a free download), available online and also via the iPad and Android apps.

Upon purchase, you will receive via email the code and instructions on how to access this product.

Time limit

The eBooks products do not have an expiry date. You will continue to access your digital ebook products whilst you have your Bookshelf installed.