# Explorative Imitation Learning: A Path Signature Approach for Continuous Environments

arXiv: https://arxiv.org/abs/2407.04856 · HTML: https://mlsanity.com/paper/2407.04856/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Placed in Imitation learning methods, one of 13 directions in Offline RL and imitation learning from data.
- Builds on 16 of its 28 references that are in the map.
- First posted on arXiv 5 Jul 2024.

## Placed in

- Offline RL and imitation learning from data / [Imitation learning methods](https://mlsanity.com/offlinerl/imitation-learning-methods/)

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

- [High-Dimensional Continuous Control Using Generalized Advantage Estimation](https://mlsanity.com/paper/1506.02438/) (2015)
- [Generative Adversarial Imitation Learning](https://mlsanity.com/paper/1606.03476/) (2016)
- [Cyclical Learning Rates for Training Neural Networks](https://mlsanity.com/paper/1506.01186/) (2015)
- [Behavioral Cloning from Observation](https://mlsanity.com/paper/1805.01954/) (2018)
- [A Primer on the Signature Method in Machine Learning](https://mlsanity.com/paper/1603.03788/) (2016)
- [Imitating Latent Policies from Observation](https://mlsanity.com/paper/1805.07914/) (2018)
- [Of Moments and Matching: A Game-Theoretic Framework for Closing the Imitation Gap](https://mlsanity.com/paper/2103.03236/) (2021)
- [Off-Policy Imitation Learning from Observations](https://mlsanity.com/paper/2102.13185/) (2021)
- [Signatory: differentiable computations of the signature and logsignature transforms, on both CPU and GPU](https://mlsanity.com/paper/2001.00706/) (2020)
- [MobILE: Model-Based Imitation Learning From Observation Alone](https://mlsanity.com/paper/2102.10769/) (2021)
- [Developing the Path Signature Methodology and its Application to Landmark-based Human Action Recognition](https://mlsanity.com/paper/1707.03993/) (2017)
- [RIDM: Reinforced Inverse Dynamics Modeling for Learning from a Single Observed Demonstration](https://mlsanity.com/paper/1906.07372/) (2019)
- [A Survey of Imitation Learning Methods, Environments and Metrics](https://mlsanity.com/paper/2404.19456/) (2024)
- [Imitating Unknown Policies via Exploration](https://mlsanity.com/paper/2008.05660/) (2020)
- [Augmented Behavioral Cloning from Observation](https://mlsanity.com/paper/2004.13529/) (2020)
- [Imitation Learning Datasets: A Toolkit For Creating Datasets, Training Agents and Benchmarking](https://mlsanity.com/paper/2403.00550/) (2024)

## Built on by (most cited first, 3 of 3)

- [Mixture of Autoencoder Experts Guidance using Unlabeled and Incomplete Data for Exploration in Reinforcement Learning](https://mlsanity.com/paper/2507.15287/) (2025)
- [Quantifying Generalisation in Imitation Learning](https://mlsanity.com/paper/2509.24784/) (2025)
- [Online Goal Recognition using Path Signature and Dynamic Time Warping](https://mlsanity.com/paper/2605.07736/) (2026)

## Abstract (arXiv)

Some imitation learning methods combine behavioural cloning with self-supervision to infer actions from state pairs. However, most rely on a large number of expert trajectories to increase generalisation and human intervention to capture key aspects of the problem, such as domain constraints. In this paper, we propose Continuous Imitation Learning from Observation (CILO), a new method augmenting imitation learning with two important features: (i) exploration, allowing for more diverse state transitions, requiring less expert trajectories and resulting in fewer training iterations; and (ii) path signatures, allowing for automatic encoding of constraints, through the creation of non-parametric representations of agents and expert trajectories. We compared CILO with a baseline and two leading imitation learning methods in five environments. It had the best overall performance of all methods in all environments, outperforming the expert in two of them.