Doubly Robust Off-policy Value And Gradient Estimation For Deterministic Policies

Abstract

Offline reinforcement learning, wherein one uses off-policy data logged by a fixed behavior policy to evaluate and learn new policies, is crucial in applications where experimentation is limited such as medicine. We study the estimation of policy value and gradient of a deterministic policy from off-policy data when actions are continuous. Targeting deterministic policies, for which action is a deterministic function of state, is crucial since optimal policies are always deterministic (up to ties). In this setting, standard importance sampling and doubly robust estimators for policy value and gradient fail because the density ratio does not exist. To circumvent this issue, we propose several new doubly robust estimators based on different kernelization approaches. We analyze the asymptotic mean-squared error of each of these under mild rate conditions for nuisance estimators. Specifically, we demonstrate how to obtain a rate that is independent of the horizon length.

Stats

• citations0
• S2 citations—
• github stars0
• HF likes0
• heat score0.00
• arxiv keykallus2020doubly

Related papers

• Doubly Robust Interval Estimation For Optimal Policy Evaluation In Online Learning (2021)0.00
• Efficient Evaluation Of Natural Stochastic Policies In Offline Reinforcement Learning (2020)0.00
• Doubly Robust Off-policy Actor-critic Algorithms For Reinforcement Learning (2019)0.00
• Statistically Efficient Variance Reduction With Double Policy Estimation For Off-policy Evaluation In Sequence-modeled Reinforcement Learning (2023)0.00
• Diverse Randomized Value Functions: A Provably Pessimistic Approach For Offline Reinforcement Learning (2024)3.58
• Online Off-policy Prediction (2018)0.00
• Hybrid Value Estimation For Off-policy Evaluation And Offline Reinforcement Learning (2022)0.00
• Conservative Bayesian Model-based Value Expansion For Offline Policy Optimization (2022)0.00