A general framework for reducing variance in agent evaluation
In this work, we present a unified, general approach to variance reduction in agent evaluation using machine learning to minimize variance. Evaluating an agent's performance in a stochastic setting is necessary for agent development, scientific evaluation, and competitions. Traditionally, evalu...
| Main Author: | |
|---|---|
| Other Authors: | |
| Format: | Others |
| Language: | en |
| Published: |
2010
|
| Subjects: | |
| Online Access: | http://hdl.handle.net/10048/890 |