Online Learning in Budget-Constrained Dynamic Colonel Blotto Games
Vincent Leon, S. Rasoul Etesami

TL;DR
This paper introduces an online learning approach for resource allocation in dynamic Colonel Blotto games, providing algorithms with sublinear regret bounds and validating them through simulations.
Contribution
It develops a novel algorithm combining combinatorial bandits and bandits with knapsacks for dynamic CBG under budget constraints, with theoretical regret guarantees.
Findings
Regret bounds are sublinear in time horizon.
Algorithm effectively handles budget constraints.
Simulations confirm theoretical results.
Abstract
In this paper, we study the strategic allocation of limited resources using a Colonel Blotto game (CBG) under a dynamic setting and analyze the problem using an online learning approach. In this model, one of the players is a learner who has limited troops to allocate over a finite time horizon, and the other player is an adversary. In each round, the learner plays a one-shot Colonel Blotto game with the adversary and strategically determines the allocation of troops among battlefields based on past observations. The adversary chooses its allocation action randomly from some fixed distribution that is unknown to the learner. The learner's objective is to minimize its regret, which is the difference between the cumulative reward of the best mixed strategy and the realized cumulative reward by following a learning algorithm while not violating the budget constraint. The learning in…
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Taxonomy
TopicsGame Theory and Applications · Advanced Bandit Algorithms Research · Reinforcement Learning in Robotics
