Minimax Value Interval for Off-Policy Evaluation and Policy Optimization
This work addresses trustworthiness and unification in off-policy evaluation for reinforcement learning, offering incremental improvements by building on prior methods.
The paper tackles the problem of quantifying biases and unifying two styles of minimax methods for off-policy evaluation, resulting in a single value interval with double robustness that provides valid intervals and quantifies misspecification.
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.