On Evaluating Agent Performance in a Fixed Period of Time

DOI

10.2991/agi.2010.41How to use a DOI?

Abstract

The evaluation of several agents over a given task in a finite period of time is a very common problem in experimental design, statistics, computer science, economics and, in general, any experimental science. It is also crucial for intelligence evaluation. In reinforcement learning, the task is formalised as an interactive environment with observations, actions and rewards. Typically, the decision that has to be made by the agent is a choice among a set of actions, cycle after cycle. However, in real evaluation scenarios, the time can be intentionally modulated by the agent. Consequently, agents not only choose an action but they also choose the time when they want to perform an action. This is natural in biological systems but it is also an issue in control. In this paper we revisit the classical reward aggregating functions which are commonly used in reinforcement learning and related areas, we analyse their problems, and we propose a modifcation of the average reward to get a consistent measurement for continuous time.

Copyright

© 2010, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - José Hernandez-Orallo
PY  - 2010/06
DA  - 2010/06
TI  - On Evaluating Agent Performance in a Fixed Period of Time
BT  - Proceedings of the 3d Conference on Artificial General Intelligence (2010)
PB  - Atlantis Press
SP  - 194
EP  - 199
SN  - 1951-6851
UR  - https://doi.org/10.2991/agi.2010.41
DO  - 10.2991/agi.2010.41
ID  - Hernandez-Orallo2010/06
ER  -