Predictability Maximization: Empowerment As An Intelligence Measure
Intelligence is often associated with the ability to optimize the environment for maximizing one's objectives (e.g. survival). In particular, the ability to predictably change the environment -- empowerment -- is an essential skill that allows agents to efficiently achieve many goals. In this talk, I will discuss empowerment from multiple perspectives, including model-based and classic goal-based RL, and relate it to classic and recently-proposed definitions and measures of intelligence.
Empowerment = mutual information between actions and future states
Maximizing empowerment = maximizing diversity of futures achievable given all actions + maximizing predictability of the future given each possible action
Empowerment could be a more direct measure of general intelligence
Shane Gu is a Research Scientist at Google Brain, where he mainly works on problems in deep learning, reinforcement learning, robotics, and probabilistic machine learning. His recent research focuses on sample-efficient RL methods that could scale to solve difficult continuous control problems in the real-world, which have been covered by Google Research Blogpost and MIT Technology Review. He completed his PhD in Machine Learning at the University of Cambridge and the Max Planck Institute for Intelligent Systems in Tübingen, where he was co-supervised by Richard E. Turner, Zoubin Ghahramani, and Bernhard Schölkopf. During his PhD, he also collaborated closely with Sergey Levine at UC Berkeley/Google Brain and Timothy Lillicrap at DeepMind. He holds a B.ASc. in Engineering Science from the University of Toronto, where he did his thesis with Geoffrey Hinton in distributed training of neural networks using evolutionary algorithms.