# Learning Representative Trajectories of Dynamical Systems via Domain-Adaptive Imitation

arXiv: https://arxiv.org/abs/2304.10260 · HTML: https://mlsanity.com/paper/2304.10260/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 16 of its 52 references that are in the map.
- First posted on arXiv 19 Apr 2023.

## Builds on (most cited first, 16 of 16)

- [Improved Training of Wasserstein GANs](https://mlsanity.com/paper/1704.00028/) (2017)
- [OpenAI Gym](https://mlsanity.com/paper/1606.01540/) (2016)
- [Deep Reinforcement Learning at the Edge of the Statistical Precipice](https://mlsanity.com/paper/2108.13264/) (2021)
- [A Brief Survey of Deep Reinforcement Learning](https://mlsanity.com/paper/1708.05866/) (2017)
- [NIPS 2016 Tutorial: Generative Adversarial Networks](https://mlsanity.com/paper/1701.00160/) (2016)
- [Learning Agile Robotic Locomotion Skills by Imitating Animals](https://mlsanity.com/paper/2004.00784/) (2020)
- [Time2Vec: Learning a Vector Representation of Time](https://mlsanity.com/paper/1907.05321/) (2019)
- [Discriminator-Actor-Critic: Addressing Sample Inefficiency and Reward Bias in Adversarial Imitation Learning](https://mlsanity.com/paper/1809.02925/) (2018)
- [Learning to Walk via Deep Reinforcement Learning](https://mlsanity.com/paper/1812.11103/) (2018)
- [Cross-domain Imitation from Observations](https://mlsanity.com/paper/2105.10037/) (2021)
- [Combating Mode Collapse in GAN training: An Empirical Analysis using Hessian Eigenvalues](https://mlsanity.com/paper/2012.09673/) (2020)
- [SFV: Reinforcement Learning of Physical Skills from Videos](https://mlsanity.com/paper/1810.03599/) (2018)
- [Learning to Fly via Deep Model-Based Reinforcement Learning](https://mlsanity.com/paper/2003.08876/) (2020)
- [Sequential Anomaly Detection using Inverse Reinforcement Learning](https://mlsanity.com/paper/2004.10398/) (2020)
- [Towards General and Autonomous Learning of Core Skills: A Case Study in Locomotion](https://mlsanity.com/paper/2008.12228/) (2020)
- [Optimal Any-Angle Pathfinding on a Sphere](https://mlsanity.com/paper/2004.12781/) (2020)

## Abstract (arXiv)

Domain-adaptive trajectory imitation is a skill that some predators learn for survival, by mapping dynamic information from one domain (their speed and steering direction) to a different domain (current position of the moving prey). An intelligent agent with this skill could be exploited for a diversity of tasks, including the recognition of abnormal motion in traffic once it has learned to imitate representative trajectories. Towards this direction, we propose DATI, a deep reinforcement learning agent designed for domain-adaptive trajectory imitation using a cycle-consistent generative adversarial method. Our experiments on a variety of synthetic families of reference trajectories show that DATI outperforms baseline methods for imitation learning and optimal control in this setting, keeping the same per-task hyperparameters. Its generalization to a real-world scenario is shown through the discovery of abnormal motion patterns in maritime traffic, opening the door for the use of deep reinforcement learning methods for spatially-unconstrained trajectory data mining.