OffSim: Offline Simulator for Model-based Offline Inverse Reinforcement Learning

• URL: http://arxiv.org/abs/2510.15495v1
• Date: Fri, 17 Oct 2025 10:07:55 GMT
• Title: OffSim: Offline Simulator for Model-based Offline Inverse Reinforcement Learning
• Authors: Woo-Jin Ahn, Sang-Ryul Baek, Yong-Jun Lee, Hyun-Duck Choi, Myo-Taeg Lim,
• Abstract summary: OffSim is a novel model-based offline inverse reinforcement learning framework.<n>It emulates environmental dynamics and reward structure directly from expert-generated state-action trajectories.<n>OffSim can subsequently train a policy offline without further interaction with the real environment.
• Score: 8.478536100809693
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Reinforcement learning algorithms typically utilize an interactive simulator (i.e., environment) with a predefined reward function for policy training. Developing such simulators and manually defining reward functions, however, is often time-consuming and labor-intensive. To address this, we propose an Offline Simulator (OffSim), a novel model-based offline inverse reinforcement learning (IRL) framework, to emulate environmental dynamics and reward structure directly from expert-generated state-action trajectories. OffSim jointly optimizes a high-entropy transition model and an IRL-based reward function to enhance exploration and improve the generalizability of the learned reward. Leveraging these learned components, OffSim can subsequently train a policy offline without further interaction with the real environment. Additionally, we introduce OffSim$^+$, an extension that incorporates a marginal reward for multi-dataset settings to enhance exploration. Extensive MuJoCo experiments demonstrate that OffSim achieves substantial performance gains over existing offline IRL methods, confirming its efficacy and robustness.

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