Multi-agent bandit with agent-dependent expected rewards

Author:

Jiang Fan,Cheng Hui

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence

Reference33 articles.

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2. Agrawal, S., & Goyal, N. (2013). Further optimal regret bounds for thompson sampling. Artificial intelligence and statistics, Vol. 31 (pp. 99–107).

3. Anandkumar, A., Michael, N., Tang, A. K., & Swami, A. (2011). Distributed algorithms for learning and cognitive medium access with logarithmic regret. IEEE Journal on Selected Areas in Communications., 29(4), 731–745.

4. Bistritz, I., & Leshem, A. (2018). Distributed multi-player bandits-a game of thrones approach. Advances in Neural Information Processing Systems, 31 (pp. 7222–723).

5. Boucheron, S., Lugosi, G., & Massart, P. (2016). Concentration inequalities: A nonasymptotic theory of independence. Oxford University Press.

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