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

Woo-Jin Ahn, Sang-Ryul Baek, Yong-Jun Lee, Hyun-Duck Choi, Myo-Taeg Lim · Oct 17, 2025 · Citations: 0

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Mar 25, 2026, 3:07 PM

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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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Field Provenance & Confidence

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Evidence snippet: Reinforcement learning algorithms typically utilize an interactive simulator (i.e., environment) with a predefined reward function for policy training.

Evaluation Modes

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Simulation environment

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Evidence snippet: Reinforcement learning algorithms typically utilize an interactive simulator (i.e., environment) with a predefined reward function for policy training.

Quality Controls

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Evidence snippet: Reinforcement learning algorithms typically utilize an interactive simulator (i.e., environment) with a predefined reward function for policy training.

Benchmarks / Datasets

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Evidence snippet: Reinforcement learning algorithms typically utilize an interactive simulator (i.e., environment) with a predefined reward function for policy training.

Reported Metrics

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Evidence snippet: Reinforcement learning algorithms typically utilize an interactive simulator (i.e., environment) with a predefined reward function for policy training.

Rater Population

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Evidence snippet: 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.

Human Data Lens

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  • Abstract highlights: 3 key sentence(s) extracted below.

Evaluation Lens

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  • Potential evaluation modes: Simulation environment
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Research Brief

Deterministic synthesis

Reinforcement learning algorithms typically utilize an interactive simulator (i.e., environment) with a predefined reward function for policy training.

Generated Mar 25, 2026, 3:07 PM · Grounded in abstract + metadata only

Key Takeaways

  • 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.

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