The simulation of user behavior with deep reinforcement learning agents has shown some recent success. However, the inverse problem, that is, inferring the free parameters of the simulator from observed user behaviors, remains challenging to solve. This is because the optimization of the new action policy of the simulated agent, which is required whenever the model parameters change, is computationally impractical. In this study, we introduce a network modulation technique that can obtain a generalized policy that immediately adapts to the given model parameters. Further, we demonstrate that the proposed technique improves the efficiency of user simulator-based inference by eliminating the need to obtain an action policy for novel model parameters. We validated our approach using the latest user simulator for point-and-click behavior. Consequently, we succeeded in inferring the user's cognitive parameters and intrinsic reward settings with less than 1/1000 computational power to those of existing methods.
|Title of host publication||CHI 2022 - Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems|
|Publisher||Association for Computing Machinery|
|Publication status||Published - 2022 Apr 29|
|Event||2022 CHI Conference on Human Factors in Computing Systems, CHI 2022 - Virtual, Online, United States|
Duration: 2022 Apr 30 → 2022 May 5
|Name||Conference on Human Factors in Computing Systems - Proceedings|
|Conference||2022 CHI Conference on Human Factors in Computing Systems, CHI 2022|
|Period||22/4/30 → 22/5/5|
Bibliographical noteFunding Information:
This work was supported in part by the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (NRF-2018R1D1A1B07043580), in part by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (NRF-2020R1A2C400214612), and in part by the Institute of Information and Communications Technology Planning and Evaluation (IITP) grant funded by the Korea government (MSIT) (2020-0-01361).
© 2022 ACM.
All Science Journal Classification (ASJC) codes
- Human-Computer Interaction
- Computer Graphics and Computer-Aided Design