Minimax Value Interval for Off-Policy Evaluation and Policy Optimization

• URL: http://arxiv.org/abs/2002.02081v6
• Date: Wed, 4 Nov 2020 23:43:32 GMT
• Title: Minimax Value Interval for Off-Policy Evaluation and Policy Optimization
• Authors: Nan Jiang, Jiawei Huang
• Abstract summary: We study minimax methods for off-policy evaluation using value functions and marginalized importance weights. Despite that they hold promises of overcoming the exponential variance in traditional importance sampling, several key problems remain. For the sake of trustworthy OPE, is there anyway to quantify the biases?
• Score: 28.085288472120705
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We study minimax methods for off-policy evaluation (OPE) using value functions and marginalized importance weights. Despite that they hold promises of overcoming the exponential variance in traditional importance sampling, several key problems remain: (1) They require function approximation and are generally biased. For the sake of trustworthy OPE, is there anyway to quantify the biases? (2) They are split into two styles ("weight-learning" vs "value-learning"). Can we unify them? In this paper we answer both questions positively. By slightly altering the derivation of previous methods (one from each style; Uehara et al., 2020), we unify them into a single value interval that comes with a special type of double robustness: when either the value-function or the importance-weight class is well specified, the interval is valid and its length quantifies the misspecification of the other class. Our interval also provides a unified view of and new insights to some recent methods, and we further explore the implications of our results on exploration and exploitation in off-policy policy optimization with insufficient data coverage.

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