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CAEPIA
2011
Springer

Evaluating a Reinforcement Learning Algorithm with a General Intelligence Test

7 years 6 months ago
Evaluating a Reinforcement Learning Algorithm with a General Intelligence Test
In this paper we apply the recent notion of anytime universal intelligence tests to the evaluation of a popular reinforcement learning algorithm, Q-learning. We show that a general approach to intelligence evaluation of AI algorithms is feasible. This top-down (theory-derived) approach is based on a generation of environments under a Solomonoff universal distribution instead of using a pre-defined set of specific tasks, such as mazes, problem repositories, etc. This first application of a general intelligence test to a reinforcement learning algorithm brings us to the issue of task-specific vs. general AI agents. This, in turn, suggests new avenues for AI agent evaluation and AI competitions, and also conveys some further insights about the performance of specific algorithms.
Javier Insa-Cabrera, David L. Dowe, José He
Added 13 Dec 2011
Updated 13 Dec 2011
Type Journal
Year 2011
Where CAEPIA
Authors Javier Insa-Cabrera, David L. Dowe, José Hernández-Orallo
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