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Optimal Cooperative Multiplayer Learning Bandits with Noisy Rewards and No Communication
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abstract
We consider a cooperative multiplayer bandit learning problem where the players are only allowed to agree on a strategy beforehand, but cannot communicate during the learning process. In this problem, each player simultaneously selects an action. Based on the actions selected by all players, the team of players receives a reward. The actions of all the players are commonly observed. However, each player receives a noisy version of the reward which cannot be shared with other players. Since players receive potentially different rewards, there is an asymmetry in the information used to select their actions. In this paper, we provide an algorithm based on upper and lower confidence bounds that the players can use to select their optimal actions despite the asymmetry in the reward information. We show that this algorithm can achieve logarithmic $O(\frac{\log T}{\Delta_{\bm{a}}})$ (gap-dependent) regret as well as $O(\sqrt{T\log T})$ (gap-independent) regret. This is asymptotically optimal in $T$. We also show that it performs empirically better than the current state of the art algorithm for this environment.
Forward citations
Cited by 5 Pith papers
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Robust Multi-Agent Bandits with Heavy-Tailed Rewards and Information Asymmetry
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Cooperative multiplayer Lipschitz bandits with an unknown Lipschitz constant achieve T^(Md+1)/(Md+2)-scale regret in three information structures, with dithering synchronizing the players' discretizations.
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A decentralized UCB variant learns unknown coordination thresholds and avoids decoy arms, reportedly approaching oracle-level cumulative reward in a small simulated environment.
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DCM Bandits: Multiplayer Information Asymmetric Cascading Bandits for Multiple Clicks
A new multiplayer formulation of Dependent Click Model bandits with action and reward asymmetry, three decentralized algorithms, and sublinear regret guarantees.
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Decentralized Multi-Player Q-Learning in Episodic Markov Decision Processes with Information Asymmetry
Decentralized players using pre-agreed deterministic tie-breaking can match centralized Q-learning regret when either actions or rewards are shared, but the fully asymmetric setting rests on an exploration argument th...
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