Denoised Imitation Learning based on Domain Adaptation for Robust Policy Learning from Noisy Demonstrations

Core Concepts

DIDA designs two discriminators to distinguish the noise level and expertise level of data, facilitating a feature encoder to learn task-related but domain-agnostic representations, enabling robust imitation learning from noisy expert demonstrations.

Abstract

The paper focuses on the problem of Learning from Noisy Demonstrations (LND), where the imitator is required to learn from data with noise that often occurs during the processes of data collection or transmission. Previous imitation learning methods improve the robustness of learned policies by injecting an adversarially learned Gaussian noise into pure expert data or utilizing additional ranking information, but they may fail in the LND setting.

To address this, the authors propose Denoised Imitation learning based on Domain Adaptation (DIDA), which designs two discriminators to distinguish the noise level and expertise level of data, facilitating a feature encoder to learn task-related but domain-agnostic representations. DIDA can successfully handle challenging imitation tasks from demonstrations with various types of noise, outperforming most baseline methods.

The key components of DIDA include:

1. A noise discriminator Dn and a policy discriminator Dp, which work together to eliminate domain information from the state embeddings learned by the feature encoder Gf.
2. A domain adversarial sampling (DAS) technique to extract samples with less domain information from the imitator buffer for training the policy discriminator.
3. A self-adaptive rate (SAR) to dynamically adjust the proportion of imitator samples in the mixed training batch, improving the performance and stability of the algorithm.
4. A new method to construct the anchor buffer ˜BA by shuffling the noisy expert buffer, which is more practical than collecting random data in the expert domain.

Experiments on 10 distinctive tasks with various types of noise on MuJoCo show the desirable performance of DIDA on LND problems, outperforming most baseline methods.

Stats

The expert policy achieves a test return of 1813.6 ± 590.5 on Hopper and 122.8 ± 1.7 on Swimmer.
The authors add various types of noise, including Gaussian, Normal, Shuffle, Doubly-stochastic, and Combined noise, to the expert demonstrations to simulate noisy data.

Quotes

"Noise is inevitable in the real world. Therefore, making agents (also called imitators) robust to noise is crucial to the applications of IL methods."
"Previous robust IL methods generally improve the robustness of the learned policy by injecting non-expert data or adversarially learned Gaussian noise into pure expert data. Some of which use additional ranking information. However, in most real-world scenarios, noisy expert data is more accessible than pure expert data, thus the LND setting that we propose is more realistic."

Key Insights Distilled From

DIDA

by Kaichen Huan... at arxiv.org 04-05-2024

https://arxiv.org/pdf/2404.03382.pdf

Deeper Inquiries

How can DIDA be extended to handle time-varying or nonlinear noise in the real world

To extend DIDA to handle time-varying or nonlinear noise in the real world, several modifications and enhancements can be made to the existing framework. One approach could involve incorporating dynamic noise models that can adapt to changes in the noise characteristics over time. This would require the feature encoder in DIDA to learn representations that are not only domain-agnostic but also capable of capturing the evolving nature of the noise. Additionally, introducing recurrent neural networks or other sequential models could help in modeling the temporal aspects of the noise, enabling the imitator to adapt to variations in the noise profile. By training the model on a diverse set of noisy data with varying temporal patterns, DIDA can learn to generalize to different types of time-varying noise effectively.

How can DIDA be adapted to address task-relevant noise that is inherent in the environment, in addition to task-irrelevant noise introduced during data transmission

Adapting DIDA to address task-relevant noise that is inherent in the environment alongside task-irrelevant noise would require a more sophisticated approach. One strategy could involve designing a hierarchical noise modeling framework where the feature encoder can differentiate between task-relevant and task-irrelevant noise components. By incorporating additional discriminators or auxiliary tasks that focus on identifying and filtering out task-relevant noise, DIDA can learn to extract the essential information for imitation learning while disregarding the noise that is not relevant to the task. This hierarchical noise modeling approach would enable DIDA to handle complex noise structures present in real-world environments and improve the robustness of the learned policies.

What other domain adaptation techniques could be explored to further improve the performance of DIDA on LND problems

To further enhance the performance of DIDA on Learning from Noisy Demonstrations (LND) problems, exploring other domain adaptation techniques could be beneficial. One potential technique is adversarial domain adaptation, where the imitator is trained to align the distributions of noisy expert data and imitator data in a shared feature space. By introducing domain adversarial training or domain confusion objectives, DIDA can learn domain-invariant representations that are robust to different types of noise. Another approach could involve self-supervised domain adaptation, where the imitator leverages self-supervised learning tasks to learn domain-agnostic features from the noisy data. By incorporating self-supervised objectives such as pretext tasks or contrastive learning, DIDA can improve its ability to generalize to noisy environments and enhance its performance on challenging imitation tasks.