Dual Alignment Maximin Optimization for Offline Model-based RL

• URL: http://arxiv.org/abs/2502.00850v1
• Date: Sun, 02 Feb 2025 16:47:35 GMT
• Title: Dual Alignment Maximin Optimization for Offline Model-based RL
• Authors: Chi Zhou, Wang Luo, Haoran Li, Congying Han, Tiande Guo, Zicheng Zhang,
• Abstract summary: offline reinforcement agents face significant deployment challenges due to the synthetic-to-real distribution mismatch. In this paper, we first shift the focus from model reliability to policy discrepancies while optimizing for expected returns, and then self-consistently incorporate synthetic data. It is a unified framework to ensure both model-environment policy consistency and synthetic and data offline.
• Score: 10.048622079413313
• License:
• Abstract: Offline reinforcement learning agents face significant deployment challenges due to the synthetic-to-real distribution mismatch. While most prior research has focused on improving the fidelity of synthetic sampling and incorporating off-policy mechanisms, the directly integrated paradigm often fails to ensure consistent policy behavior in biased models and underlying environmental dynamics, which inherently arise from discrepancies between behavior and learning policies. In this paper, we first shift the focus from model reliability to policy discrepancies while optimizing for expected returns, and then self-consistently incorporate synthetic data, deriving a novel actor-critic paradigm, Dual Alignment Maximin Optimization (DAMO). It is a unified framework to ensure both model-environment policy consistency and synthetic and offline data compatibility. The inner minimization performs dual conservative value estimation, aligning policies and trajectories to avoid out-of-distribution states and actions, while the outer maximization ensures that policy improvements remain consistent with inner value estimates. Empirical evaluations demonstrate that DAMO effectively ensures model and policy alignments, achieving competitive performance across diverse benchmark tasks.

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