# Decentralized policy learning with partial observation and mechanical constraints for multiperson modeling

arXiv: https://arxiv.org/abs/2007.03155 · HTML: https://mlsanity.com/paper/2007.03155/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Jul 2020.

## Built on by (most cited first, 10 of 10)

- [Evaluation of creating scoring opportunities for teammates in soccer via trajectory prediction](https://mlsanity.com/paper/2206.01899/) (2022)
- [Estimating counterfactual treatment outcomes over time in complex multiagent scenarios](https://mlsanity.com/paper/2206.01900/) (2022)
- [Action valuation of on- and off-ball soccer players based on multi-agent deep reinforcement learning](https://mlsanity.com/paper/2305.17886/) (2023)
- [OpenSTARLab: Open Approach for Spatio-Temporal Agent Data Analysis in Soccer](https://mlsanity.com/paper/2502.02785/) (2025)
- [Mathematical models for off-ball scoring prediction in basketball](https://mlsanity.com/paper/2406.08749/) (2024)
- [TrackID3x3: A Dataset and Algorithm for Multi-Player Tracking with Identification and Pose Estimation in 3x3 Basketball Full-court Videos](https://mlsanity.com/paper/2503.18282/) (2025)
- [Evaluating Movement Initiation Timing in Ultimate Frisbee via Temporal Counterfactuals](https://mlsanity.com/paper/2508.17611/) (2025)
- [Expandable Decision-Making States for Multi-Agent Deep Reinforcement Learning in Soccer Tactical Analysis](https://mlsanity.com/paper/2510.00480/) (2025)
- [Data-driven simulator of multi-animal behavior with unknown dynamics via offline and online reinforcement learning](https://mlsanity.com/paper/2510.10451/) (2025)
- [Strategic Multi-Agent Learning for Interpretable Action Valuation of All Players in Football](https://mlsanity.com/paper/2610.05961/) (2026)

## Abstract (arXiv)

Extracting the rules of real-world multi-agent behaviors is a current challenge in various scientific and engineering fields. Biological agents independently have limited observation and mechanical constraints; however, most of the conventional data-driven models ignore such assumptions, resulting in lack of biological plausibility and model interpretability for behavioral analyses. Here we propose sequential generative models with partial observation and mechanical constraints in a decentralized manner, which can model agents' cognition and body dynamics, and predict biologically plausible behaviors. We formulate this as a decentralized multi-agent imitation-learning problem, leveraging binary partial observation and decentralized policy models based on hierarchical variational recurrent neural networks with physical and biomechanical penalties. Using real-world basketball and soccer datasets, we show the effectiveness of our method in terms of the constraint violations, long-term trajectory prediction, and partial observation. Our approach can be used as a multi-agent simulator to generate realistic trajectories using real-world data.