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Resource allocation games such as the famous Colonel Blotto (CB) andHide-and-Seek (HS) games are often used to model a large variety of practicalproblems, but only in their one-shot versions. Indeed, due to their extremelylarge strategy space, it remains an open question how one can efficiently learnin these games. In this work, we show that the online CB and HS games can becast as path planning problems with side-observations (SOPPP): at each stage, alearner chooses a path on a directed acyclic graph and suffers the sum oflosses that are adversarially assigned to the corresponding edges; and she thenreceives semi-bandit feedback with side-observations (i.e., she observes thelosses on the chosen edges plus some others). We propose a novel algorithm,EXP3-OE, the first-of-its-kind with guaranteed efficient running time for SOPPPwithout requiring any auxiliary oracle. We provide an expected-regret bound ofEXP3-OE in SOPPP matching the order of the best benchmark in the literature.Moreover, we introduce additional assumptions on the observability model underwhich we can further improve the regret bounds of EXP3-OE. We illustrate thebenefit of using EXP3-OE in SOPPP by applying it to the online CB and HS games.

Path Planning Problems with Side Observations-When Colonels Play Hide-and-Seek