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Evidence record 18696 · automatically gathered

IADD-TR: Intervention-Aware Dynamics Decoupling with Targeted Regularization for Model-Based Reinforcement Learning

Model-based reinforcement learning (MBRL), which learns environment dynamics to generate synthetic experience, is a promising approach to sample-efficient decision making. Numerous methods have been developed to improve dynamics prediction and policy optimization for MBRL through uncertainty estimation, model regularization, and conservative value learning. However, these methods typically treat the transition model and critic as monolithic predictors, overlooking the policy-induced data bias. C

Record details

Published: 11 August 2026
Source: arXiv cs.LG
Category: Research
Topics: Bias & fairness · Regulation · Environment
Retrieved: 12 August 2026

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How to cite this record

ethics.ai (11 August 2026), “IADD-TR: Intervention-Aware Dynamics Decoupling with Targeted Regularization for Model-Based Reinforcement Learning,” evidence record 18696, https://ethics.ai/record/18696 (originally published by arXiv cs.LG).

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