Abstract
We consider active inference as a novel approach to the design of synthetic autonomous agents. In order to assess active inference’s feasibility for real-world applications, we developed an agent that controls a ground-based robot. The agent contains a generative dynamic model for the robot’s position and for performance appraisals by an observer of the robot. Our experiments show that the agent is capable of learning the target parking position from the observer’s feedback and robustly steer the robot toward the learned target position.
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Acknowledgements
This work was partly financed by research programmes ZERO and EDL with project numbers P15-06 and P16-25 respectively, which are both (partly) financed by the Netherlands Organisation for Scientific Research (NWO).
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Ergul, B., van de Laar, T., Koudahl, M., Roa-Villescas, M., de Vries, B. (2020). Learning Where to Park. In: Verbelen, T., Lanillos, P., Buckley, C.L., De Boom, C. (eds) Active Inference. IWAI 2020. Communications in Computer and Information Science, vol 1326. Springer, Cham. https://doi.org/10.1007/978-3-030-64919-7_14
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DOI: https://doi.org/10.1007/978-3-030-64919-7_14
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