Comparison of Data Amount for Representing Decision Making Policy
Ueda Ryuichi · IOS Press eBooks · 2008
This paper deals with the problem of how to implement software controller for a robot with a small amount of random access memory (RAM) on its computer. This problem is essentially different from how to solve it with a small amount of RAM. This paper purely compares the trade-off between memory use and performance of a controller. We found that policies that are compressed by vector quantization have an efficient representation.