Neural Dueling Bandits: Principled Preference-Based Optimization with Non-Linear Reward Function

ICLR 2025 Conference Submission13697 Authors

Keywords: Contextual Duling Bandits, Preferences Learning, Neural Bandits

TL;DR: We study contextual dueling bandits problem and propose upper confidence bound- and Thompson sampling-based algorithms that use a neural network to estimate the reward function using only preference feedback and have sub-linear regret guarantees.

Abstract: Contextual dueling bandit is used to model the bandit problems, where a learner's goal is to find the best arm for a given context using observed noisy preference feedback over the selected arms for the past contexts. However, existing algorithms assume the reward function is linear, which can be complex and non-linear in many real-life applications like online recommendations or ranking web search results. To overcome this challenge, we use a neural network to estimate the reward function using preference feedback for the previously selected arms. We propose upper confidence bound- and Thompson sampling-based algorithms with sub-linear regret guarantees that efficiently select arms in each round. We also extend our theoretical results to contextual bandit problems with binary feedback, which is in itself a non-trivial contribution. Experimental results on the problem instances derived from synthetic datasets corroborate our theoretical results.

Primary Area: reinforcement learning

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Submission Number: 13697