Kernelized Offline Contextual Dueling Bandits

• URL: http://arxiv.org/abs/2307.11288v1
• Date: Fri, 21 Jul 2023 01:17:31 GMT
• Title: Kernelized Offline Contextual Dueling Bandits
• Authors: Viraj Mehta and Ojash Neopane and Vikramjeet Das and Sen Lin and Jeff Schneider and Willie Neiswanger
• Abstract summary: In this work, we take advantage of the fact that often the agent can choose contexts at which to obtain human feedback. We give an upper-confidence-bound style algorithm for this setting and prove a regret bound.
• Score: 15.646879026749168
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Preference-based feedback is important for many applications where direct evaluation of a reward function is not feasible. A notable recent example arises in reinforcement learning from human feedback on large language models. For many of these applications, the cost of acquiring the human feedback can be substantial or even prohibitive. In this work, we take advantage of the fact that often the agent can choose contexts at which to obtain human feedback in order to most efficiently identify a good policy, and introduce the offline contextual dueling bandit setting. We give an upper-confidence-bound style algorithm for this setting and prove a regret bound. We also give empirical confirmation that this method outperforms a similar strategy that uses uniformly sampled contexts.

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