Reinforcement learning and the future of software

In the last few years, deep learning has achieved significant success in a wide range of domains, including computer vision, artificial intelligence, speech, NLP, and reinforcement learning. However, deep learning in recommender systems has, until recently, received relatively little attention. Nick Pentreath explores recent advances in this area in both research and practice.

Deep reinforcement learning is a thriving area and has wide applications in industry. Arsenii Mustafin shares his experience developing deep reinforcement learning applications on BigDL and Spark.

Danny Lange offers an overview of deep reinforcement learning, an exciting new chapter in AIs history that is changing the way we develop and test learning algorithms that can later be used in real life.

Mark Hammond explores a wide breadth of real-world applications of deep reinforcement learning, including robotics, manufacturing, energy, and supply chain. Mark also shares best practices and tips for building and deploying these systems, highlighting the unique requirements and challenges of industrial AI applications.

Reinforcement learning approaches can be massively parallelized, so Kubernetes seems like a natural fit, as Kubernetes is all about reducing the overhead for managing applications. However, it can be …

Tomer Kaftan offers an overview of Cuttlefish, a lightweight framework prototyped in Apache Spark that helps developers adaptively improve the performance of their data processing applications by inserting a few library calls into their code. These calls construct tuning primitives that use reinforcement learning to adaptively modify execution as they observe application performance over time.