Reinforcement Learning with Interactive World Models for Adaptive Control in Complex Robotic Systems
Keywords:
reinforcement learning; interactive world models; adaptive control; robotics; model-based learning; system architecture; safety; governance; sustainability; human oversightAbstract
This paper examines reinforcement learning with interactive world models as a system-level architecture for adaptive control in complex robotic systems. Rather than treating world models as passive predictors, the analysis positions them as dynamic infrastructural components that mediate perception, planning, action, memory, and governance. The paper considers how interactive world models can improve sample efficiency, enable counterfactual reasoning, and support safer adaptation in unstructured environments. It also identifies structural trade-offs involving model fidelity, computational cost, interpretability, latency, and maintainability. The discussion extends beyond algorithmic performance to deployment realities, including fleet learning, edge-cloud coordination, human oversight, accountability, sustainability, and equitable access to robotic infrastructure. By comparing model-based reinforcement learning with model-free methods and with related developments in simulation, digital twins, and autonomous decision systems, the paper argues that interactive world models are most effective when embedded in layered control architectures that preserve human authority and operational transparency. The conclusion emphasizes that adaptive robotic control is not merely a technical optimization problem but a socio-technical design challenge requiring robust governance, lifecycle evaluation, and policy frameworks that align autonomy with public values.
References
1. Sutton, R. S., & Barto, A. G. (2018). Reinforcement learning: An introduction (2nd ed.). MIT Press.
2. Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A. A., Veness, J., Bellemare, M. G., Graves, A., Riedmiller, M., Fidjeland, A. K., Ostrovski, G., Petersen, S., Beattie, C., Sadik, A., Antonoglou, I., King, H., Kumaran, D., Wierstra, D., Legg, S., & Hassabis, D. (2015). Human-level control through deep reinforcement learning. Nature, 518(7540), 529-533.
3. Lillicrap, T. P., Hunt, J. J., Pritzel, A., Heess, N., Erez, T., Tassa, Y., Silver, D., & Wierstra, D. (2016). Continuous control with deep reinforcement learning. In International Conference on Learning Representations.
4. Schulman, J., Wolski, F., Dhariwal, P., Radford, A., & Klimov, O. (2017). Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347.
5. Ha, D., & Schmidhuber, J. (2018). Recurrent world models facilitate policy evolution. In Advances in Neural Information Processing Systems (pp. 2450-2462).
6. Hafner, D., Lillicrap, T., Ba, J., & Norouzi, M. (2019). Dream to control: Learning behaviors by latent imagination. In International Conference on Learning Representations.
7. Kaiser, L., Babaeizadeh, M., Milos, P., Osinski, B., Campbell, R. H., Czechowski, K., Erhan, D., Finn, C., Kozakowski, P., Levine, S., Mohiuddin, A., Sepassi, R., Tucker, G., & Michalewski, H. (2020). Model-based reinforcement learning for Atari. In International Conference on Learning Representations.
8. Xiong, Zhexiao, et al. "ActWorld: From Explorable to Interactive World Model via Action-Aware Memory." arXiv preprint arXiv:2606.17730 (2026).
9. Levine, S., Finn, C., Darrell, T., & Abbeel, P. (2016). End-to-end training of deep visuomotor policies. Journal of Machine Learning Research, 17(39), 1-40.
10. Ibarz, J., Tan, J., Finn, C., Kalakrishnan, M., Pastor, P., & Levine, S. (2021). How to train your robot with deep reinforcement learning: Lessons we have learned. The International Journal of Robotics Research, 40(4-5), 698-721.
11. Kober, J., Bagnell, J. A., & Peters, J. (2013). Reinforcement learning in robotics: A survey. The International Journal of Robotics Research, 32(11), 1238-1274.
12. Chua, K., Calandra, R., McAllister, R., & Levine, S. (2018). Deep reinforcement learning in a handful of trials using probabilistic dynamics models. In Advances in Neural Information Processing Systems (pp. 4754-4765).
13. Janner, M., Fu, J., Zhang, M., & Levine, S. (2019). When to trust your model: Model-based policy optimization. In Advances in Neural Information Processing Systems (pp. 12519-12530).
14. Nagabandi, A., Konolige, K., Levine, S., & Kumar, V. (2020). Deep dynamics models for learning dexterous manipulation. In Conference on Robot Learning (pp. 1101-1112).
15. Silver, D., Huang, A., Maddison, C. J., Guez, A., Sifre, L., van den Driessche, G., Schrittwieser, J., Antonoglou, I., Panneershelvam, V., Lanctot, M., Dieleman, S., Grewe, D., Nham, J., Kalchbrenner, N., Sutskever, I., Lillicrap, T., Leach, M., Kavukcuoglu, K., Graepel, T., & Hassabis, D. (2016). Mastering the game of Go with deep neural networks and tree search. Nature, 529(7587), 484-489.
16. Silver, D., Schrittwieser, J., Simonyan, K., Antonoglou, I., Huang, A., Guez, A., Hubert, T., Baker, L., Lai, M., Bolton, A., Chen, Y., Lillicrap, T., Hui, F., Sifre, L., van den Driessche, G., Graepel, T., & Hassabis, D. (2017). Mastering the game of Go without human knowledge. Nature, 550(7676), 354-359.
17. Amodei, D., Olah, C., Steinhardt, J., Christiano, P., Schulman, J., & Mané, D. (2016). Concrete problems in AI safety. arXiv preprint arXiv:1606.06565.
18. Sheridan, T. B. (2016). Human-robot interaction: Status and challenges. Human Factors, 58(4), 525-532.
19. Shneiderman, B. (2020). Human-centered artificial intelligence: Reliable, safe and trustworthy. International Journal of Human-Computer Interaction, 36(6), 495-504.
20. Leike, J., Martic, M., Krakovna, V., Ortega, P. A., Everitt, T., Lefrancq, A., Orseau, L., & Legg, S. (2017). AI safety gridworlds. arXiv preprint arXiv:1711.09883.
21. Raji, I. D., Smart, A., White, R. N., Mitchell, M., Gebru, T., Hutchinson, B., Smith-Loud, J., Theron, D., & Barnes, P. (2020). Closing the AI accountability gap: Defining an end-to-end framework for internal algorithmic auditing. In Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency (pp. 33-44).
22. Dafoe, A., Hughes, E., Bachrach, Y., Collins, T., McKee, K. R., Leibo, J. Z., Larson, K., & Graepel, T. (2020). Open problems in cooperative AI. arXiv preprint arXiv:2012.08630.
23. Kaelbling, L. P., Littman, M. L., & Cassandra, A. R. (1998). Planning and acting in partially observable stochastic domains. Artificial Intelligence, 101(1-2), 99-134.
24. Brunke, L., Greeff, M., Hall, A. W., Yuan, Z., Zhou, S., Panerati, J., & Schoellig, A. P. (2022). Safe learning in robotics: From learning-based control to safe reinforcement learning. Annual Review of Control, Robotics, and Autonomous Systems, 5, 411-444.
25. Dulac-Arnold, G., Levine, N., Mankowitz, D. J., Li, J., Paduraru, C., Gowal, S., & Hester, T. (2021). Challenges of real-world reinforcement learning: Definitions, benchmarks and analysis. Machine Learning, 110(9), 2419-2468.
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