Estimating Agent Skill in Continuous Action Domains

Abstract

Actions in most real-world continuous domains cannot be executed exactly. An agent’s performance in these domains is influenced by two critical factors: the ability to select effective actions (decision-making skill), and how precisely it can execute those selected actions (execution skill). This article addresses the problem of estimating the execution and decision-making skill of an agent, given observations. Several execution skill estimation methods are presented, each of which utilize different information from the observations and make assumptions about the agent’s decision-making ability. A final novel method forgoes these assumptions about decision-making and instead estimates the execution and decision-making skills simultaneously under a single Bayesian framework. Experimental results in several domains evaluate the estimation accuracy of the estimators, especially focusing on how robust they are as agents and their decision-making methods are varied. These results demonstrate that reasoning about both types of skill together significantly improves the robustness and accuracy of execution skill estimation. A case study is presented using the proposed methods to estimate the skill of Major League Baseball pitchers, demonstrating how these methods can be applied to real-world data sources.