Adaptive Exploration for Latent-State Bandits

• URL: http://arxiv.org/abs/2602.05139v1
• Date: Wed, 04 Feb 2026 23:49:39 GMT
• Title: Adaptive Exploration for Latent-State Bandits
• Authors: Jikai Jin, Kenneth Hung, Sanath Kumar Krishnamurthy, Baoyi Shi, Congshan Zhang,
• Abstract summary: We introduce a family of state-model-free bandit algorithms that leverage lagged contextual features and coordinated probing strategies.<n>These implicitly track latent states and disambiguate state-dependent reward patterns.<n> Empirical results across diverse settings demonstrate superior performance over classical approaches.
• Score: 7.757117209804723
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The multi-armed bandit problem is a core framework for sequential decision-making under uncertainty, but classical algorithms often fail in environments with hidden, time-varying states that confound reward estimation and optimal action selection. We address key challenges arising from unobserved confounders, such as biased reward estimates and limited state information, by introducing a family of state-model-free bandit algorithms that leverage lagged contextual features and coordinated probing strategies. These implicitly track latent states and disambiguate state-dependent reward patterns. Our methods and their adaptive variants can learn optimal policies without explicit state modeling, combining computational efficiency with robust adaptation to non-stationary rewards. Empirical results across diverse settings demonstrate superior performance over classical approaches, and we provide practical recommendations for algorithm selection in real-world applications.

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