Gal Novik offers an overview of Reinforcement Learning Coach, an open source Python library that models the interaction between an agent and an environment in a modular way, making it easy for researchers to implement new reinforcement learning algorithms and for data scientists to integrate additional simulation environments modeling their business problems.

An AI + Network proposal called Intelligence Driven Network will be proposed. The network, especially the OAM, will develop toward intelligent and high-level automatic. The next hop of Internet potent …

We haven't figured out how to make the perfect robot tutors. But we have figured out how make them much more effective in improving student learning outcomes with modern AI techniques. Varun Arora covers some of those important techniques, along with real-world examples.

Jian Wu discusses an end-to-end engineering project to train and evaluate deep Q-learning models for targeting sequential marketing campaigns using the 10-fold cross-validation method. Jian also explains how to evaluate trained DQN models with neural network-based baseline models and shows that trained deep Q-learning models generally produce better-optimized long-term rewards.

Danny Goodman discusses 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.