This video, a follow up from his intro video on reinforcement learning, Arxiv dives into three advanced papers that address the problem of the sparse reward setting in Deep Reinforcement Learning and pose interesting research directions for mastering unsupervised learning in autonomous agents.
In this video, Siraj Raval explores how key reinforcement learning algorithms help explain how the human brain works, specifically through the lens of the neurotransmitter known as ‘dopamine’.
These algorithms have been used to help train everything from autopilot systems for airplanes, to video game bots. TD-Learning, Rescorla-Wagner, Kalman Filters, and Bayesian Learning, all in one go!
By leveraging powerful prior knowledge about how the world works, humans can quickly figure out efficient strategies in new and unseen environments.
Currently, even state-of-the-art Reinforcement Learning algorithms typically don’t have strong priors and this is one of the fundamental challenges in current research on Transfer Learning.
Check out this great demo of the new way to configure message routing in Azure IoT Hub using Device Twin properties and the updated UI in the Azure portal.
Paul Montgomery, Senior Software Engineer in the Azure IoT Team shows us how this all works using an ESP32 based microcontroller, a temperature sensor, a glass of hot water and another glass of iced water.
Live Q&A with the Mark Russinovich, CTO of Azure, at Microsoft Ignite 2018.