This thesis investigates the effect of communication as a function of time on a
multi-agent simulation based on a military distillation utilizing reinforcement learning
for a group of agents. The original contribution to knowledge is a new model of
cooperative learning developed as an enhanced Q-learning update function which also
includes learning events communicated by other agents. Further contributions lie in the
detailed analysis of simulation results establishing evidence of the cause-effect
relationship between communication and improved performance. The improvement in
performance is visualized by utilizing surface plot diagrams of the agents state-action
matrix. These diagrams show the how group communications reinforce effective
actions for the agents at an early stage in the simulation.