Sparse representations for reinforcement learning

人工智能系统的一个关键组成部分是处理和学习高维、大容量感官信息流的能力。例如，控制工厂泵的代理不断接收有关温度和能耗的感官信息，从而不断实时调整电机速度以优化性能。为了做出这样的决定，代理人需要能够预测其行为的长期结果。例如，如果工业代理可以预测电机的长期温度，给定系统的当前状态，他们可以使用这些预测来改进他们的决策，并确保电机不损坏。

A key component of an artificial intelligence system is the ability to process and learn from a high-dimensional, high-volume sensory stream of information. For example, an agent controlling the pumps in an industrial plant continually receives sensory information about temperatures and energy consumption, to continually adjust the motor speed in real-time to optimize performance. To make such decision, the agents needs to be able to predict the long-term outcomes of their behaviour. For example, if the industrial agent can predict the long-term temperature of the motor, given the current state of the system, they can use these predictions to improve their decisions and ensure motors are not damaged. Such predictions, however, can be difficult to learn accurately from raw sensory information. Predictions are typically learned as functions of inputted sensory information. For example, the prediction of motor temperature in five minutes could be approximated as a polynomial function of the last ten recorded temperature and motor speeds. Polynomials, however, are only one possible functional form, and not necessarily the best one. Further, to obtain general learning agents, the functional forms should be effective across multiple settings or tasks. This is the goal of representation learning in reinforcement learning: identifying a general mapping from a sequence of raw sensory information to a set of features, that facilitates accurate predictions. The goal in my research is to understandboth theoretically and empiricallythe properties of effective representations for a reinforcement learning agent learning on a continual stream of sensory information. A part of this challenge is to identify simpler representations for which we can provide optimization guarantees, but that are nonetheless sufficiently powerful to facilitate learning. Continuing preliminary research, I will explore prototype-based (kernel) representations and a sparse supervised auto-encoder representation. We have already found that, within this class of simpler representations, we can find computational models that provide highly accurate predictions, but are more amenable to theoretical analysis. A core component of this research direction will be to investigate sparsity as a generally useful property of representations, and how we can encode that property into our representation learning algorithms. If successful, this research will have important scientific and societal benefits. This research will contribute to a core endeavour in artificial intelligence: understanding how to develop intelligent agents that can learn in complex environments. This understanding, in turn, will contribute to improving the robustness of automated decision-making systems, which are becoming ubiquitous in our world, including in industrial systems and factories, in self-driving vehicles and even in our homes.