  In several reinforcement learning (RL) scenarios, mainly in security
settings, there may be adversaries trying to interfere with the reward
generating process. In this paper, we introduce Threatened Markov Decision
Processes (TMDPs), which provide a framework to support a decision maker
against a potential adversary in RL. Furthermore, we propose a level-$k$
thinking scheme resulting in a new learning framework to deal with TMDPs. After
introducing our framework and deriving theoretical results, relevant empirical
evidence is given via extensive experiments, showing the benefits of accounting
for adversaries while the agent learns.
