  Reinforcement learning has shown much success in games such as chess,
backgammon and Go. However, in most of these games, agents have full knowledge
of the environment at all times. In this paper, we describe a deep learning
model in which agents successfully adapt to different classes of opponents and
learn the optimal counter-strategy using reinforcement learning in a game under
partial observability. We apply our model to FlipIt, a two-player security game
in which both players, the attacker and the defender, compete for ownership of
a shared resource and only receive information on the current state of the game
upon making a move. Our model is a deep neural network combined with Q-learning
and is trained to maximize the defender's time of ownership of the resource.
Despite the noisy information, our model successfully learns a cost-effective
counter-strategy outperforming its opponent's strategies and shows the
advantages of the use of deep reinforcement learning in game theoretic
scenarios. We also extend FlipIt to a larger action-spaced game with the
introduction of a new lower-cost move and generalize the model to $n$-player
FlipIt.
