Abstract
In many collaborative environments, optimal coordination depends on implicit preferences that are not directly observable. This work explores inverse reinforcement learning (IRL) to infer latent reward structures from observed interaction trajectories. A maximum entropy IRL framework is combined with policy optimization to align agent behavior with inferred preferences. The method is validated on 7,800 decision trajectories collected from simulated negotiation and resource allocation environments. Results show that preference-aware policies improve alignment with target outcomes by 21.3% and reduce conflict frequency by 25.7% compared to manually designed reward functions. Furthermore, inferred reward models generalize across similar tasks with less than 10% performance loss. These findings indicate that IRL provides an effective mechanism for capturing hidden objectives in collaborative systems.
References
Acharya, D. B., Kuppan, K., & Divya, B. (2025). Agentic AI: Autonomous intelligence for complex goals—A comprehensive survey. IEEe Access, 13, 18912-18936.
Xu, D., Liu, H., Qiu, D., & Ma, Q. (2026). Structured Modeling and Representation Methods for Post-Retrieval Inference Processes in Large Video Language Models.
Mehrabi, N., Boroujeni, S. P. H., & Razi, A. (2026). Turbo-IRL: Enhancing multi-agent systems using turbo decoding-inspired deep maximum entropy inverse reinforcement learning. Expert Systems with Applications, 296, 128754.
Liu, S., & Yim, J. (2025). Research on Generative AI Creation Systems Based on Visual Language Modeling: Human-Machine Collaboration and Cognitive Feedback Mechanisms. Available at SSRN 6139770.
Broukhim, A., Shen, Y., Ammanabrolu, P., & Weibel, N. (2025). Preference-Based Learning in Audio Applications: A Systematic Analysis. arXiv preprint arXiv:2511.13936.
Xu, D., Chen, H., & Gui, H. (2026). Unified Online Estimation Method for SOC, SOH, and Power Capacity Considering Safety Boundary Consistency in Battery Management Systems.
Alipanah, A. (2025). Vulnerabilities in Maximum Entropy Inverse Reinforcement Learning under Adversarial Demonstrations.
Wang, Y., Yin, X., Chen, J., & Wang, Y. (2026). Evidence-Based Study on Low-Burden Digital Phenotyping for Precision Screening of Oral Anti-Obesity Drug Efficacy.
Rafailov, R., Sharma, A., Mitchell, E., Manning, C. D., Ermon, S., & Finn, C. (2023). Direct preference optimization: Your language model is secretly a reward model. Advances in neural information processing systems, 36, 53728-53741.
Zhang, Y., Gu, W., & Wang, J. (2025). Research on First Article Inspection (FAI)-Driven Quality Assurance Methods for Wind Turbine Installation and Operation & Maintenance and Their Effect on Reliability Improvement. Available at SSRN 6094206.
Maisto, D., Donnarumma, F., & Pezzulo, G. (2023). Interactive inference: A multi-agent model of cooperative joint actions. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 54(2), 704-715.
Liu, S., Liu, X., & Feng, H. (2025, November). Research on AI-Driven Visual Design and Immersive Interactive Experiences Based on Multimodal Cognition and User. In Proceedings of the 2025 International Conference on Digital Society and Intelligent Computing (pp. 734-740).
da Silva Soares, P. R. (2024). Probabilistic Modeling of False Beliefs and Team Coordination (Doctoral dissertation, The University of Arizona).
Qiu, D., Xu, D., & Yue, L. (2025, December). Reinforcement Learning-Augmented LLM Agents for Collaborative Decision Making and Performance Optimization. In 2025 7th International Conference on Frontier Technologies of Information and Computer (ICFTIC) (pp. 1337-1342). IEEE.
Chandra, R., Karnan, H., Mehr, N., Stone, P., & Biswas, J. (2025, October). Multi-agent inverse reinforcement learning in real world unstructured pedestrian crowds. In 2025 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (pp. 18668-18675). IEEE.
Gao, G., Gao, R., Gao, R., & Zhou, H. (2026). Engineering Analysis and Quantitative Research on the Platform-Based Evolution of Enterprise Communication Systems.
Bennett, T. J., O’Neill, S. K., & Harding, L. M. (2026). Multi-Agent Reinforcement Learning for Cooperative Large Language Model Collaboration. Global Media and Social Sciences Research Journal, 7(1), 225-233.
Zhang, Y., Gu, W., & Wang, J. (2026). Construction of Wind Farm Asset Health Index Based on Multi-Dimensional Indicators and Analytic Hierarchy Process and Its Correlation with Operational Performance. Authorea Preprints.
Swamy, G., Wu, D., Choudhury, S., Bagnell, D., & Wu, S. (2023, July). Inverse reinforcement learning without reinforcement learning. In International Conference on Machine Learning (pp. 33299-33318). PMLR.
Jiao, Y., Wang, A., Zhao, B., & Shi, T. (2026). The Impact of Visual Language Strategies in Public Art Creation on Community Spatial Perception and Public Behavior.
Alakuijala, M., Dulac-Arnold, G., Mairal, J., Ponce, J., & Schmid, C. (2023, May). Learning reward functions for robotic manipulation by observing humans. In 2023 IEEE International Conference on Robotics and Automation (ICRA) (pp. 5006-5012). IEEE.
Wang, Y., Chen, J., Wang, Y., & Yin, X. (2026). Application of Obtainable Biological Agent Characteristics in Efficacy Stratification of Oral Anti-Obesity Drugs.
Ibrahim, S., Mostafa, M., Jnadi, A., Salloum, H., & Osinenko, P. (2024). Comprehensive overview of reward engineering and shaping in advancing reinforcement learning applications. IEEE Access, 12, 175473-175500.
Xu, D., Gui, H., & Chen, H. (2026). Research on Layered Control and Fault Recovery Mechanisms for Fast Charging Safety Diagnosis of High Voltage Battery Systems Under Charging Network Interoperability Conditions.
This work is licensed under a Creative Commons Attribution 4.0 International License.
Copyright (c) 2026 Liam O’Connor, Matthew J. Williams, Daniel Smith (Author)