Data-driven simulator of multi-animal behavior with unknown dynamics via offline and online reinforcement learning

• URL: http://arxiv.org/abs/2510.10451v1
• Date: Sun, 12 Oct 2025 05:08:26 GMT
• Title: Data-driven simulator of multi-animal behavior with unknown dynamics via offline and online reinforcement learning
• Authors: Keisuke Fujii, Kazushi Tsutsui, Yu Teshima, Makoto Itoh, Naoya Takeishi, Nozomi Nishiumi, Ryoya Tanaka, Shunsuke Shigaki, Yoshinobu Kawahara,
• Abstract summary: A key challenge for realistic multi-animal simulation in biology is bridging the gap between unknown real-world transition models and their simulated counterparts.<n>We introduce a data-driven simulator for multi-animal behavior based on deep reinforcement learning and counterfactual simulation.
• Score: 8.835312357110618
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Simulators of animal movements play a valuable role in studying behavior. Advances in imitation learning for robotics have expanded possibilities for reproducing human and animal movements. A key challenge for realistic multi-animal simulation in biology is bridging the gap between unknown real-world transition models and their simulated counterparts. Because locomotion dynamics are seldom known, relying solely on mathematical models is insufficient; constructing a simulator that both reproduces real trajectories and supports reward-driven optimization remains an open problem. We introduce a data-driven simulator for multi-animal behavior based on deep reinforcement learning and counterfactual simulation. We address the ill-posed nature of the problem caused by high degrees of freedom in locomotion by estimating movement variables of an incomplete transition model as actions within an RL framework. We also employ a distance-based pseudo-reward to align and compare states between cyber and physical spaces. Validated on artificial agents, flies, newts, and silkmoth, our approach achieves higher reproducibility of species-specific behaviors and improved reward acquisition compared with standard imitation and RL methods. Moreover, it enables counterfactual behavior prediction in novel experimental settings and supports multi-individual modeling for flexible what-if trajectory generation, suggesting its potential to simulate and elucidate complex multi-animal behaviors.

Related papers

• D-REX: Differentiable Real-to-Sim-to-Real Engine for Learning Dexterous Grasping [66.22412592525369]
  We introduce a real-to-sim-to-real engine that leverages the Gaussian Splat representations to build a differentiable engine.<n>We show that our engine achieves accurate and robust performance in mass identification across various object geometries and mass values.<n>Those optimized mass values facilitate force-aware policy learning, achieving superior and high performance in object grasping.
  arXiv  Detail & Related papers  (2026-03-01T15:32:04Z)
• The Sound of Simulation: Learning Multimodal Sim-to-Real Robot Policies with Generative Audio [138.07247714782412]
  MultiGen is a framework that integrates large-scale generative models into traditional physics simulators.<n>We demonstrate effective zero-shot transfer to real-world pouring with novel containers and liquids.
  arXiv  Detail & Related papers  (2025-07-03T17:59:58Z)
• Pre-Trained Video Generative Models as World Simulators [59.546627730477454]
  We propose Dynamic World Simulation (DWS) to transform pre-trained video generative models into controllable world simulators.<n>To achieve precise alignment between conditioned actions and generated visual changes, we introduce a lightweight, universal action-conditioned module.<n> Experiments demonstrate that DWS can be versatilely applied to both diffusion and autoregressive transformer models.
  arXiv  Detail & Related papers  (2025-02-10T14:49:09Z)
• LASIL: Learner-Aware Supervised Imitation Learning For Long-term Microscopic Traffic Simulation [30.500368103677097]
  Microscopic traffic simulation plays a crucial role in transportation engineering by providing insights into individual vehicle behavior and overall traffic flow. Traditional simulators relying on models often fail to deliver accurate simulations due to the complexity of real-world traffic environments. In this paper, we propose a novel approach called learner-aware supervised imitation learning.
  arXiv  Detail & Related papers  (2024-03-26T11:13:35Z)
• Learning Quadruped Locomotion Using Differentiable Simulation [31.80380408663424]
  Differentiable simulation promises fast convergence and stable training. This work proposes a new differentiable simulation framework to overcome these challenges. Our framework enables learning quadruped walking in simulation in minutes without parallelization.
  arXiv  Detail & Related papers  (2024-03-21T22:18:59Z)
• Waymax: An Accelerated, Data-Driven Simulator for Large-Scale Autonomous Driving Research [76.93956925360638]
  Waymax is a new data-driven simulator for autonomous driving in multi-agent scenes. It runs entirely on hardware accelerators such as TPUs/GPUs and supports in-graph simulation for training. We benchmark a suite of popular imitation and reinforcement learning algorithms with ablation studies on different design decisions.
  arXiv  Detail & Related papers  (2023-10-12T20:49:15Z)
• User Behavior Simulation with Large Language Model based Agents [116.74368915420065]
  We propose an LLM-based agent framework and design a sandbox environment to simulate real user behaviors. Based on extensive experiments, we find that the simulated behaviors of our method are very close to the ones of real humans.
  arXiv  Detail & Related papers  (2023-06-05T02:58:35Z)
• From Model-Based to Data-Driven Simulation: Challenges and Trends in Autonomous Driving [26.397030011439163]
  We provide an overview of challenges with regard to different aspects and types of simulation. We cover aspects around perception-, behavior- and content-realism as well as general hurdles in the domain of simulation. Among others, we observe a trend of data-driven, generative approaches and high-fidelity data synthesis to increasingly replace model-based simulation.
  arXiv  Detail & Related papers  (2023-05-23T11:39:23Z)
• Real-World Humanoid Locomotion with Reinforcement Learning [92.85934954371099]
  We present a fully learning-based approach for real-world humanoid locomotion. Our controller can walk over various outdoor terrains, is robust to external disturbances, and can adapt in context.
  arXiv  Detail & Related papers  (2023-03-06T18:59:09Z)
• OstrichRL: A Musculoskeletal Ostrich Simulation to Study Bio-mechanical Locomotion [8.849771760994273]
  We release a 3D musculoskeletal simulation of an ostrich based on the MuJoCo simulator. The model is based on CT scans and dissections used to gather actual muscle data. We also provide a set of reinforcement learning tasks, including reference motion tracking and a reaching task with the neck.
  arXiv  Detail & Related papers  (2021-12-11T19:58:11Z)
• TrafficSim: Learning to Simulate Realistic Multi-Agent Behaviors [74.67698916175614]
  We propose TrafficSim, a multi-agent behavior model for realistic traffic simulation. In particular, we leverage an implicit latent variable model to parameterize a joint actor policy. We show TrafficSim generates significantly more realistic and diverse traffic scenarios as compared to a diverse set of baselines.
  arXiv  Detail & Related papers  (2021-01-17T00:29:30Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.