POTEC: Off-Policy Learning for Large Action Spaces via Two-Stage Policy Decomposition

• URL: http://arxiv.org/abs/2402.06151v1
• Date: Fri, 9 Feb 2024 03:01:13 GMT
• Title: POTEC: Off-Policy Learning for Large Action Spaces via Two-Stage Policy Decomposition
• Authors: Yuta Saito, Jihan Yao, Thorsten Joachims
• Abstract summary: We study off-policy learning of contextual bandit policies in large discrete action spaces. We propose a novel two-stage algorithm, called Policy Optimization via Two-Stage Policy Decomposition. We show that POTEC provides substantial improvements in OPL effectiveness particularly in large and structured action spaces.
• Score: 40.851324484481275
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We study off-policy learning (OPL) of contextual bandit policies in large discrete action spaces where existing methods -- most of which rely crucially on reward-regression models or importance-weighted policy gradients -- fail due to excessive bias or variance. To overcome these issues in OPL, we propose a novel two-stage algorithm, called Policy Optimization via Two-Stage Policy Decomposition (POTEC). It leverages clustering in the action space and learns two different policies via policy- and regression-based approaches, respectively. In particular, we derive a novel low-variance gradient estimator that enables to learn a first-stage policy for cluster selection efficiently via a policy-based approach. To select a specific action within the cluster sampled by the first-stage policy, POTEC uses a second-stage policy derived from a regression-based approach within each cluster. We show that a local correctness condition, which only requires that the regression model preserves the relative expected reward differences of the actions within each cluster, ensures that our policy-gradient estimator is unbiased and the second-stage policy is optimal. We also show that POTEC provides a strict generalization of policy- and regression-based approaches and their associated assumptions. Comprehensive experiments demonstrate that POTEC provides substantial improvements in OPL effectiveness particularly in large and structured action spaces.

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