Get hands-on experience in creating state-of-the-art reinforcement learning agents using
TensorFlow and RLlib to solve complex real-world business and industry problems with the …

Reinforcement learning (RL) has proven its worth in a series of artificial domains, and is
beginning to show some successes in real-world scenarios. However, much of the research …

Offline reinforcement learning (RL) can be used to improve future performance by
leveraging historical data. There exist many different algorithms for offline RL, and it is well …

Recent technology development brings the booming of numerous new Demand-Driven
Services (DDS) into urban lives, including ridesharing, on-demand delivery, express …

The goal of reinforcement learning (RL) is to build an autonomous agent that takes a
sequence of actions to maximize a utility function by interacting with an external, unknown …

Reinforcement Learning (RL) is crucial for data-driven decisionmaking but suffers from
sample inefficiency. This poses a risk to system safety and can be costly in real-world …

Deep reinforcement learning (DRL) combines deep learning (DL) with a reinforcement
learning (RL) architecture. It has been able to perform a wide range of complex decision …

Reinforcement learning (RL) algorithms can successfully solve a wide range of problems
that we faced. Because of the Alpha Go against KeJie in 2017, the topic of RL has reached …

Sepsis, a life-threatening illness, is estimated to be the primary cause of death for 50,000
people a year in the UK and many more worldwide. Managing the treatment of sepsis is very …

Recently deep reinforcement learning has achieved tremendous success in wide ranges of
applications. However, it notoriously lacks data-efficiency and interpretability. Data-efficiency …