Abstract
We present a variant of the Q-learning algorithm with
automatic control of the exploration rate by a com-
petition scheme. The theoretical approach is accom-
panied by systematic simulations of a chaos control
task. Finally, we give interpretations of the algorithm
in the context of computational ecology and neural
networks.
| Original language | English |
|---|---|
| Pages (from-to) | 441-444 |
| Number of pages | 4 |
| Journal | Nonlinear Theory and Its Applications (NOLTA) |
| Volume | 96 |
| Publication status | Published - 1996 |