The Effect of Communication on Noncooperative Multiplayer Multi-Armed Bandit Problems
| Autor: | Alper Kose, Noyan Evirgen |
|---|---|
| Rok vydání: | 2017 |
| Předmět: |
Computer Science::Machine Learning
FOS: Computer and information sciences Computer Science::Computer Science and Game Theory Computer science Stochastic process 020206 networking & telecommunications Regret 02 engineering and technology Multi-armed bandit Machine Learning (cs.LG) Computer Science - Learning Bernoulli distribution 0202 electrical engineering electronic engineering information engineering Graph (abstract data type) Reinforcement learning Probability distribution Mathematical economics Thompson sampling Game theory |
| Zdroj: | ICMLA |
| DOI: | 10.48550/arxiv.1711.01628 |
| Popis: | We consider decentralized stochastic multi-armed bandit problem with multiple players in the case of different communication probabilities between players. Each player makes a decision of pulling an arm without cooperation while aiming to maximize his or her reward but informs his or her neighbors in the end of every turn about the arm he or she pulled and the reward he or she got. Neighbors of players are determined according to an Erdos-Renyi graph with which is reproduced in the beginning of every turn. We consider i.i.d. rewards generated by a Bernoulli distribution and assume that players are unaware about the arms' probability distributions and their mean values. In case of a collision, we assume that only one of the players who is randomly chosen gets the reward where the others get zero reward. We study the effects of connectivity, the degree of communication between players, on the cumulative regret using well-known algorithms UCB1, epsilon-Greedy and Thompson Sampling. Comment: This work has been accepted to the 2017 IEEE ICMLA |
| Databáze: | OpenAIRE |
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