A Metric for Intrinsic Motivation in Reinforcement Learning Agents
Loading...
Date
2022-04-18
Authors
Journal Title
Journal ISSN
Volume Title
Publisher
Abstract
Classically, the reward for an agent is given by extrinsic factors which motivate the agent to improve and learn; however, an active area of research within cognitive science and AI is the effect and necessity of intrinsic motivation for an agent. This can manifest itself in many forms from curiosity to reduction of cognitive dissonance to motivation for effectance. Despite the prevalence and perceived importance of intrinsic motivation, there is no metric to measure “how” intrinsically motivated an agent is compared to another and is instead, largely empirical. Furthermore, methods that might be stated to be intrinsically motivated can be directly linked to the environment and thus, might be less intrinsically motivated than thought. Thus this thesis presents a general metric for intrinsically motivated agents to suggest that highly intrinsically motivated agents are more robust than less intrinsically motivated agents. First, an overview and review of reinforcement learning and intrinsic motivation is presented. Following this, a general metric is proposed with empirical and mathematical justification to measure the intrinsic motivation of an agent. Lastly, several intrinsic motivation agents are tested to evaluate the metric and compare the relative performance of the agents.
Description
Keywords
Artificial Intelligence, Machine Learning, Reinforcement Learning, Intrinsic Motivation