This article explores hybrid agents that use a variety of techniques to improve their performance in an environment over time. We considered, specifically, genetic-learning-parenting hybrid agents, which used a combination of a genetic algorithm, a learning algorithm (in our case, reinforcement learning), and a parenting algorithm, to modify their activity. We experimentally examined what constitutes the best combination of weights over these three algorithms, as a function of the environment's rate of change. For stationary environments, a genetic-parenting combination proved best, with genetics being given the most weight. For environments with low rates of change, genetic-learning-parenting hybrids were best, with learning having the most weight, and parenting having at least as much weight as genetics. For environments with high rates of change, pure learning agents proved best. A pure parenting algorithm operated extremely poorly in all settings.
|Original language||American English|
|Number of pages||37|
|Journal||Journal of Experimental and Theoretical Artificial Intelligence|
|State||Published - Sep 2010|
- Monte Carlo
- genetic algorithms