Foundations of Deep Reinforcement Learning-轻识

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–...

The Contemporary Introduction to Deep Reinforcement Learning that Combines Theory and Practice

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

劳拉·格雷泽

（Laura Graesser）

软件工程师，在谷歌从事机器人技术方面的工作。她拥有纽约大学计算机科学硕士学位，专攻机器学习方向。

龚辉伦

（Wah Loon Keng）

Machine Zone的人工智能工程师，致力于将深度强化学习应用于工业问题。他拥有理论物理和计算机科学的背景。

他们共同开发了两个深度强化学习软件库，并就此进行了多次主题讲座和技术辅导。

主要译者：

许静

南开大学人工智能学院副院长，机器智能所所长，教授，博士生导师。2003年获得南开大学博士学位，主要研究方向为人工智能、大数据分析和软件安全。已完成多项国jia级、省部级、国际合作、国内合作项目，发表学术论文100余篇，出版教材一部，申请发明专利20余项；获得天津市科技进步二等奖两项；现为IEEE会员、CCF高级会员、天津市图形图像学会常务理事。