STITCH-OPE: Trajectory Stitching with Guided Diffusion for Off-Policy Evaluation

• URL: http://arxiv.org/abs/2505.20781v1
• Date: Tue, 27 May 2025 06:39:26 GMT
• Title: STITCH-OPE: Trajectory Stitching with Guided Diffusion for Off-Policy Evaluation
• Authors: Hossein Goli, Michael Gimelfarb, Nathan Samuel de Lara, Haruki Nishimura, Masha Itkina, Florian Shkurti,
• Abstract summary: Off-policy evaluation (OPE) estimates the performance of a target policy using offline data collected from a behavior policy.<n>Existing OPE methods are ineffective for high-dimensional, long-horizon problems.<n>We propose STITCH-OPE, a model-based generative framework that leverages denoising diffusion for long-horizon OPE.
• Score: 18.55356623615343
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Off-policy evaluation (OPE) estimates the performance of a target policy using offline data collected from a behavior policy, and is crucial in domains such as robotics or healthcare where direct interaction with the environment is costly or unsafe. Existing OPE methods are ineffective for high-dimensional, long-horizon problems, due to exponential blow-ups in variance from importance weighting or compounding errors from learned dynamics models. To address these challenges, we propose STITCH-OPE, a model-based generative framework that leverages denoising diffusion for long-horizon OPE in high-dimensional state and action spaces. Starting with a diffusion model pre-trained on the behavior data, STITCH-OPE generates synthetic trajectories from the target policy by guiding the denoising process using the score function of the target policy. STITCH-OPE proposes two technical innovations that make it advantageous for OPE: (1) prevents over-regularization by subtracting the score of the behavior policy during guidance, and (2) generates long-horizon trajectories by stitching partial trajectories together end-to-end. We provide a theoretical guarantee that under mild assumptions, these modifications result in an exponential reduction in variance versus long-horizon trajectory diffusion. Experiments on the D4RL and OpenAI Gym benchmarks show substantial improvement in mean squared error, correlation, and regret metrics compared to state-of-the-art OPE methods.

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