Learn More
Safe state abstraction in reinforcement learning allows an agent to ignore aspects of its current state that are irrelevant to its current decision, and therefore speeds up dynamic programming and learning. This paper explores safe state abstraction in hierarchical reinforcement learning, where learned behaviors must conform to a given partial, hierarchical(More)
– The design (synthesis) of analog electrical circuits starts with a high-level statement of the circuit's desired behavior and requires creating a circuit that satisfies the specified design goals. Analog circuit synthesis entails the creation of both the topology and the sizing (numerical values) of all of the circuit's components. The difficulty of the(More)
– It would be desirable if computers could solve problems without the need for a human to write the detailed programmatic steps. That is, it would be desirable to have a domain-independent automatic programming technique in which "What You Want Is What You Get" ("WYWIWYG" – pronounced "wow-eee-wig"). Genetic programming is such a technique. This paper(More)