mlsanityData to 7 Oct 2026

Synthetically Generating Human-like Data for Sequential Decision Making Tasks via Reward-Shaped Imitation Learning

arXiv 2304.07280 ↗First posted 14 Apr 2023cs.LG cs.AIcited by 0

On the map: Offline RL and imitation learning from data → Imitation learning methods

Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in Imitation learning methods, one of 13 directions in Offline RL and imitation learning from data. Builds on 15 of its 40 references that are in the map. First posted on arXiv 14 Apr 2023.

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15 of 40 references.

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Same direction, newest first

  1. 2022imitation: Clean Imitation Learning Implementations22 Nov

Other references · 14

  1. 2014Conditional Generative Adversarial Netscited by 2,152
  2. 2016OpenAI Gymcited by 1,684
  3. 2019Modeling Tabular data using Conditional GANcited by 695
  4. 2016Conditional Image Synthesis With Auxiliary Classifier GANscited by 562
  5. 2021A Survey of Data Augmentation Approaches for NLPcited by 408
  6. 2017Data Augmentation Generative Adversarial Networkscited by 278
  7. 2018GAN-based Synthetic Medical Image Augmentation for increased CNN Performance in Liver Lesion Classificationcited by 242
  8. 2020Learning to Utilize Shaping Rewards: A New Approach of Reward Shapingcited by 112
  9. 2018BAGAN: Data Augmentation with Balancing GANcited by 90
  10. 2020Differentially Private Synthetic Medical Data Generation using Convolutional GANscited by 52
  11. 2020TrajGAIL: Generating Urban Vehicle Trajectories using Generative Adversarial Imitation Learningcited by 46
  12. 2020Improving the Accuracy of Global Forecasting Models using Time Series Data Augmentationcited by 31
2 more
  1. 2022Generative Adversarial Networks for Image Augmentation in Agriculture: A Systematic Reviewcited by 3
  2. 2022Why GANs are overkill for NLPcited by 1

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  1. 2021Modeling Strong and Human-Like Gameplay with KL-Regularized Search
  2. 2022Generative Personas That Behave and Experience Like Humans
  3. 2020Learning to Play by Imitating Humans
  4. 2023A Survey of Imitation Learning: Algorithms, Recent Developments, and Challenges
  5. 2021Imitation by Predicting Observations
  6. 2019A New Framework for Query Efficient Active Imitation Learning
  7. 2019Automated Video Game Testing Using Synthetic and Human-Like Agents
  8. 2024Behavioural Cloning in VizDoom