Satisficing vs exploring when learning a constrained environment

Satisficing is an efficient strategy for applying existing knowledge in a complex, constrained, environment. We present a set of agent-based simulations that demonstrate a higher payoff for satisficing strategies than for exploring strategies when using approximate dynamic programming methods for learning complex environments. In our constrained learning environment, satisficing agents outperformed exploring agent by approximately six percent, in terms of the number of tasks completed.