Facebook would like to have computer science faculty in AI committed to work 80% of their time in industrial jobs and 20% of their time at their university. They call this scheme “co-employment” or “dual... Continue
I’m going to close my outsider’s tour of Reinforcement Learning by announcing the release of a short survey of RL that coalesces my views from the perspectives of continuous control. Though the RL and controls... Continue
This is the thirteenth part of “An Outsider’s Tour of Reinforcement Learning.” Part 14 is here. Part 12 is here. Part 1 is here. Can poor models be used in control loops and still achieve... Continue
This is the twelfth part of “An Outsider’s Tour of Reinforcement Learning.” Part 13 is here. Part 11 is here. Part 1 is here. This series began by describing a view of reinforcement learning as... Continue
This is the eleventh part of “An Outsider’s Tour of Reinforcement Learning.” Part 12 is here. Part 10 is here. Part 1 is here. The essence of reinforcement learning is using past data to enhance... Continue
This is the tenth part of “An Outsider’s Tour of Reinforcement Learning.” Part 11 is here. Part 9 is here. Part 1 is here. Though I’ve spent the last few posts casting shade at model-free... Continue
I read three great articles over the weekend by Renee DiResta, Chris Wiggins, and Janelle Shane that touched on a topic that’s been troubling me: In machine learning, we take our cost functions for granted,... Continue
A common sticking point in contemporary reinforcement learning is how to evaluate performance on benchmarks. For a general purpose method, we’d like to demonstrate aptitude on a wide selection of test problems with minimal special... Continue
This is the ninth part of “An Outsider’s Tour of Reinforcement Learning.” Part 10 is here. Part 8 is here. Part 1 is here. Before we leave these model-free chronicles behind, let me turn to... Continue