  We investigate how effective an attacker can be when it only learns from its
victim's actions, without access to the victim's reward. In this work, we are
motivated by the scenario where the attacker wants to behave strategically when
the victim's motivations are unknown. We argue that one heuristic approach an
attacker can use is to maximize the entropy of the victim's policy. The policy
is generally not obfuscated, which implies it may be extracted simply by
passively observing the victim. We provide such a strategy in the form of a
reward-free exploration algorithm that maximizes the attacker's entropy during
the exploration phase, and then maximizes the victim's empirical entropy during
the planning phase. In our experiments, the victim agents are subverted through
policy entropy maximization, implying an attacker might not need access to the
victim's reward to succeed. Hence, reward-free attacks, which are based only on
observing behavior, show the feasibility of an attacker to act strategically
without knowledge of the victim's motives even if the victim's reward
information is protected.
