Jul 18, 2020
A life-long learning agent should learn not only to solve problems, but also to pose new problems for itself. In reinforcement learning, the starting problems are maximizing reward and predicting value, and the natural new problems are achieving subgoals and predicting what will happen next. There has been a lot of work that provides a language for learning new problems (e.g., on auxiliary tasks and general value functions), but precious little that actually learns them (e.g., McGovern on learning subgoal states). In this talk I present a general strategy for learning new problems and, moreover, for learning an endless cycle of problems and solutions, each leading to the other. I call this cycle the FOAK cycle, because it is based on Features, Options, And Knowledge, where “options” are temporally extended ways of behaving, and “knowledge” refers to an agent’s option-conditional model of the transition dynamics of the world. The new problems in the FOAK cycle are 1) to find options that attain state features and 2) to model the consequences of those options. As these problems are solved and the models are used in planning, more abstract features are formed and made the basis for new options and models, continuing the cycle. The FOAK cycle is intended to produce a model-based reinforcement learning agent with successively more abstract representations and knowledge of its world, in other words, a life-long learning agent.
The International Conference on Machine Learning (ICML) is the premier gathering of professionals dedicated to the advancement of the branch of artificial intelligence known as machine learning. ICML is globally renowned for presenting and publishing cutting-edge research on all aspects of machine learning used in closely related areas like artificial intelligence, statistics and data science, as well as important application areas such as machine vision, computational biology, speech recognition, and robotics. ICML is one of the fastest growing artificial intelligence conferences in the world. Participants at ICML span a wide range of backgrounds, from academic and industrial researchers, to entrepreneurs and engineers, to graduate students and postdocs.
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