Online Learning in Budget-Constrained Dynamic Colonel Blotto Games

Author:

Leon Vincent,Etesami S. Rasoul

Funder

Division of Electrical, Communications and Cyber Systems

Air Force Office of Scientific Research

Publisher

Springer Science and Business Media LLC

Subject

Applied Mathematics,Computational Mathematics,Computational Theory and Mathematics,Computer Graphics and Computer-Aided Design,Computer Science Applications,Statistics and Probability,Economics and Econometrics

Reference44 articles.

1. Agrawal S, Devanur NR, Li L (2016) An efficient algorithm for contextual bandits with knapsacks, and an extension to concave objectives. In: Feldman V, Rakhlin A, Shamir O (eds) 29th Annual conference on learning theory, vol 49. Proceedings of machine learning research. Columbia University, New York, New York, USA, pp 4–18

2. Agrawal S, Devanur NR (2014) Bandits with concave rewards and convex knapsacks. In: Proceedings of the fifteenth ACM conference on economics and computation. EC ’14. Association for Computing Machinery, New York, NY, USA, , pp 989–1006. https://doi.org/10.1145/2600057.2602844

3. Agrawal S, Devanur NR (2016) Linear contextual bandits with knapsacks. In: Proceedings of the 30th international conference on neural information processing systems. NIPS’16. Curran Associates Inc., Red Hook, NY, USA, pp 3458–3467

4. Ahmadinejad A, Dehghani S, Hajiaghayi M, Lucier B, Mahini H, Seddighin S (2019) From duels to battlefields: computing equilibria of Blotto and other games. Math Oper Res 44(4):1304–1325. https://doi.org/10.1287/moor.2018.0971

5. Auer P, Cesa-Bianchi N, Freund Y, Schapire RE (2002) The nonstochastic multiarmed bandit problem. SIAM J Comput 32(1):48–77. https://doi.org/10.1137/S0097539701398375

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