Multi-agent reinforcement learning agents can learn odd behaviors and act unexpectedly. This research presentation focuses on how to shape, analyze, and improve agent behavior during skirmishes in the video game environment of StarCraft II. We demonstrate agents can improve survivability with proper rewards, develop of new behaviors based on their enemies, retain combat tactics using transfer learning, and accelerate learning with collaborative expert training.