Progressive Mining of Transition Dynamics for Autonomous Control

Autonomous agents are emerging in diverse areas and many rely on reinforcement learning (RL) to learn optimal control policies by acting in the environment. This form of learning generates large amounts of transition dynamics data, which can be mined to improve the agent's understanding of the environment. There could be many uses for this data, here we focus on mining it to identify a relevant feature subspace. This is vital since RL performs poorly in high-dimensional spaces, such as those that autonomous agents would commonly face in real-world problems. This paper demonstrates the necessity and feasibility of integrating data mining into the learning process while an agent is learning, enabling it to learn to act by both acting and understanding. Doing so requires overcoming challenges regarding data quantity and quality, and difficulty measuring feature relevance with respect to the control policy. We propose the progressive mining framework to address these challenges by relying on cyclic interaction between data mining and RL. We show that a feature selection algorithm developed under this framework, PROFESS, can improve RL scalability better than a competing approach.