Off-Policy Evaluation in Markov Decision Processes under Weak Distributional Overlap

• URL: http://arxiv.org/abs/2402.08201v1
• Date: Tue, 13 Feb 2024 03:55:56 GMT
• Title: Off-Policy Evaluation in Markov Decision Processes under Weak Distributional Overlap
• Authors: Mohammad Mehrabi and Stefan Wager
• Abstract summary: We re-visit the task of off-policy evaluation in Markov decision processes (MDPs) under a weaker notion of distributional overlap. We introduce a class of truncated doubly robust (TDR) estimators which we find to perform well in this setting.
• Score: 5.0401589279256065
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Doubly robust methods hold considerable promise for off-policy evaluation in Markov decision processes (MDPs) under sequential ignorability: They have been shown to converge as $1/\sqrt{T}$ with the horizon $T$, to be statistically efficient in large samples, and to allow for modular implementation where preliminary estimation tasks can be executed using standard reinforcement learning techniques. Existing results, however, make heavy use of a strong distributional overlap assumption whereby the stationary distributions of the target policy and the data-collection policy are within a bounded factor of each other -- and this assumption is typically only credible when the state space of the MDP is bounded. In this paper, we re-visit the task of off-policy evaluation in MDPs under a weaker notion of distributional overlap, and introduce a class of truncated doubly robust (TDR) estimators which we find to perform well in this setting. When the distribution ratio of the target and data-collection policies is square-integrable (but not necessarily bounded), our approach recovers the large-sample behavior previously established under strong distributional overlap. When this ratio is not square-integrable, TDR is still consistent but with a slower-than-$1/\sqrt{T}$; furthermore, this rate of convergence is minimax over a class of MDPs defined only using mixing conditions. We validate our approach numerically and find that, in our experiments, appropriate truncation plays a major role in enabling accurate off-policy evaluation when strong distributional overlap does not hold.

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