Jonathan Mugan

AI and machine learning. Principal scientist at DeUmbra and author of The Curiosity Cycle.

RLlib for Deep Hierarchical Multiagent Reinforcement Learning

Reinforcement learning (RL) is an effective method for solving problems that require agents to learn the best way to act in complex environments. RLlib is a powerful tool for applying reinforcement learning to problems where there are multiple agents or when agents must take on multiple roles. There are many of resources for learning about …

RLlib for Deep Hierarchical Multiagent Reinforcement Learning Read More »

Microsoft Flight Simulator 2020 is an inflection point for virtual worlds and our own

Microsoft Flight Simulator (MSFS) 2020 doesn’t just feel real; it almost is real. You see the same objects and relationships when you look out the window that you would see out of a physical plane. Down there are your own cities and streets, and you can even fly into a live hurricane [1]. Because of …

Microsoft Flight Simulator 2020 is an inflection point for virtual worlds and our own Read More »

Generating Natural-Language Text with Neural Networks

Computers are illiterate. Reading requires mapping the words on a page to shared concepts in our culture and commonsense understanding, and writing requires mapping those shared concepts into other words on a page. We currently don’t know how to endow computers with a conceptual system rich enough to represent even what a small child knows, …

Generating Natural-Language Text with Neural Networks Read More »

Scroll to Top