Mostly Exploration-Free Algorithms for Contextual Bandits
| Autor: | Khashayar Khosravi, Mohsen Bayati, Hamsa Bastani |
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| Rok vydání: | 2021 |
| Předmět: |
FOS: Computer and information sciences
Computer Science - Machine Learning 050208 finance Computer science Strategy and Management 05 social sciences Machine Learning (stat.ML) Management Science and Operations Research 01 natural sciences Machine Learning (cs.LG) 010104 statistics & probability Statistics - Machine Learning 0502 economics and business 0101 mathematics Greedy algorithm Algorithm |
| Zdroj: | Management Science. 67:1329-1349 |
| ISSN: | 1526-5501 0025-1909 |
| Popis: | The contextual bandit literature has traditionally focused on algorithms that address the exploration-exploitation tradeoff. In particular, greedy algorithms that exploit current estimates without any exploration may be sub-optimal in general. However, exploration-free greedy algorithms are desirable in practical settings where exploration may be costly or unethical (e.g., clinical trials). Surprisingly, we find that a simple greedy algorithm can be rate optimal (achieves asymptotically optimal regret) if there is sufficient randomness in the observed contexts (covariates). We prove that this is always the case for a two-armed bandit under a general class of context distributions that satisfy a condition we term covariate diversity. Furthermore, even absent this condition, we show that a greedy algorithm can be rate optimal with positive probability. Thus, standard bandit algorithms may unnecessarily explore. Motivated by these results, we introduce Greedy-First, a new algorithm that uses only observed contexts and rewards to determine whether to follow a greedy algorithm or to explore. We prove that this algorithm is rate optimal without any additional assumptions on the context distribution or the number of arms. Extensive simulations demonstrate that Greedy-First successfully reduces exploration and outperforms existing (exploration-based) contextual bandit algorithms such as Thompson sampling or upper confidence bound (UCB). 62 Pages, 7 Figures |
| Databáze: | OpenAIRE |
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