Machine learning and learning theory research
Consider the following facts: NIPS submission are up 50% this year to ~4800 papers. There is significant evidence that the process of reviewing papers in machine learning is creaking under several years of exponentiating growth. Public figures often overclaim the state of AI. Money rains from the sky on ambitious startups with a good story. …
The surge of interest in reinforcement learning is great fun, but I often see confused choices in applying RL algorithms to solve problems. There are two purposes for which you might use a world simulator in reinforcement learning: Reinforcement Learning Research: You might be interested in creating reinforcement learning algorithms for the real world and …
Continue reading “Pervasive Simulator Misuse with Reinforcement Learning”
Politics is a distracting affair which I generally believe it’s best to stay out of if you want to be able to concentrate on research. Nevertheless, the US presidential election looks like something that directly politicizes the idea and process of research by damaging the association of scientists & students, funding for basic research, and …
I had a fantastic time at ICML 2016— I learned a great deal. There was far more good stuff than I could see, and it was exciting to catch up on recent advances. David Silver gave one of the best tutorials I’ve seen on his group’s recent work in “deep” reinforcement learning. I learned about …
We made a tool that you can use. It is the first general purpose reinforcement-based learning system 🙂 Reinforcement learning is much discussed these days with successes like AlphaGo. Wouldn’t it be great if Reinforcement Learning algorithms could easily be used to solve all reinforcement learning problems? But there is a well-known problem: It’s very …