  Models and games are simplified representations of the world. There are many
different kinds of models, all differing in complexity and which aspect of the
world they allow us to further our understanding of. In this paper we focus on
a specific instance of agent-based models, which uses reinforcement learning
(RL) to train the agent how to act in its environment. Reinforcement learning
agents are usually also Markov processes, which is another type of model that
can be used. We test this reinforcement learning agent in a Colonel Blotto
environment1, and measure its performance against Random agents as its
opponent. We find that the RL agent handily beats a single opponent, and still
performs quite well when the number of opponents are increased. We also analyze
the RL agent and look at what strategies it has arrived by looking at the
actions that it has given the highest and lowest Q-values. Interestingly, the
optimal strategy for playing multiple opponents is almost the complete opposite
of the optimal strategy for playing a single opponent.
