Synthetically generating human-like data for sequential decision-making tasks via reward-shaped imitation learning

Bryan Brandt; Prithviraj Dasgupta

doi:10.1117/12.2664109

13 June 2023 Synthetically generating human-like data for sequential decision-making tasks via reward-shaped imitation learning

Bryan Brandt, Prithviraj Dasgupta

Author Affiliations +

Proceedings Volume 12529, Synthetic Data for Artificial Intelligence and Machine Learning: Tools, Techniques, and Applications; 125290I (2023) https://doi.org/10.1117/12.2664109
Event: SPIE Defense + Commercial Sensing, 2023, Orlando, Florida, United States

Abstract

We consider the problem of synthetically generating data that can closely resemble human decisions made in the context of an interactive human-AI system like a computer game. We propose a novel algorithm that can generate synthetic, human-like, decision making data while starting from a very small set of decision making data collected from humans. Our proposed algorithm integrates the concept of reward shaping with an imitation learning algorithm to generate the synthetic data. We have validated our synthetic data generation technique by using the synthetically generated data as a surrogate for human interaction data to solve three sequential decision making tasks of increasing complexity within a small computer game-like setup. Different empirical and statistical analyses of our results show that the synthetically generated data can substitute the human data and perform the game-playing tasks almost indistinguishably, with very low divergence, from a human performing the same tasks.

Conference Presentation

Citation Download Citation

Bryan Brandt and Prithviraj Dasgupta "Synthetically generating human-like data for sequential decision-making tasks via reward-shaped imitation learning", Proc. SPIE 12529, Synthetic Data for Artificial Intelligence and Machine Learning: Tools, Techniques, and Applications, 125290I (13 June 2023); https://doi.org/10.1117/12.2664109

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