Metric learning for reinforcement learning agents

Abstract

A key component of any reinforcement learning algorithm is the underlying representation used by the agent. While reinforcement learning (RL) agents have typically relied on hand-coded state representations, there has been a growing interest in learning this representation. While inputs to an agent are typically fixed (i.e., state variables represent sensors on a robot), it is desirable to automatically determine the optimal relative scaling of such inputs, as well as to diminish the impact of irrelevant features. This work introduces Holler, a novel distance metric learning algorithm, and combines it with an existing instance-based RL algorithm to achieve precisely these goals. The algorithms' success is highlighted via empirical measurements on a set of six tasks within the mountain car domain.