Adaptive systems learn in dynamic environments by repeatedly sensing the world, performing an action, and receiving feedback from the environment. The area of reinforcement learning concerns agents that learn sequential behaviors from experience; however, learning in complex domains is excruciatingly slow. We are developing reinforcement learning methods that can be guided both by reinforcements provided by the environment and abstract advice provided by a human teacher. In particular, we are developing methods in which advice is given in ordinary natural language (which is translated into formal advice using a learned semantic parser). By taking advantage of general advice on actions to perform in certain situations, the agent's learning rate can be greatly accelerated. This work is related to our work on theory refinement and natural language learning.

Learning from natural-language advice and reinforcements is the topic of the PILLAR research project.