Integrating Lagrangian Neural Networks into the Dyna Framework for Reinforcement Learning
Fuente:
arXiv
Salvato in:
| Autori principali: | , , , , , , |
|---|---|
| Natura: | Preprint |
| Pubblicazione: |
2026
|
| Soggetti: | |
| Accesso online: | |
| Tags: |
Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
|
| _version_ | 1866914380063440896 |
|---|---|
| author | Das, Shreya Kumar, Kundan Iqbal, Muhammad Savolainen, Outi Baumann, Dominik Ruotsalainen, Laura Särkkä, Simo |
| author_facet | Das, Shreya Kumar, Kundan Iqbal, Muhammad Savolainen, Outi Baumann, Dominik Ruotsalainen, Laura Särkkä, Simo |
| contents | Model-based reinforcement learning (MBRL) is sample-efficient but depends on the accuracy of the learned dynamics, which are often modeled using black-box methods that do not adhere to physical laws. Those methods tend to produce inaccurate predictions when presented with data that differ from the original training set. In this work, we employ Lagrangian neural networks (LNNs), which enforce an underlying Lagrangian structure to train the model within a Dyna-based MBRL framework. Furthermore, we train the LNN using stochastic gradient-based and state-estimation-based optimizers to learn the network's weights. The state-estimation-based method converges faster than the stochastic gradient-based method during neural network training. Simulation results are provided to illustrate the effectiveness of the proposed LNN-based Dyna framework for MBRL. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_08468 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Integrating Lagrangian Neural Networks into the Dyna Framework for Reinforcement Learning Das, Shreya Kumar, Kundan Iqbal, Muhammad Savolainen, Outi Baumann, Dominik Ruotsalainen, Laura Särkkä, Simo Systems and Control Machine Learning Model-based reinforcement learning (MBRL) is sample-efficient but depends on the accuracy of the learned dynamics, which are often modeled using black-box methods that do not adhere to physical laws. Those methods tend to produce inaccurate predictions when presented with data that differ from the original training set. In this work, we employ Lagrangian neural networks (LNNs), which enforce an underlying Lagrangian structure to train the model within a Dyna-based MBRL framework. Furthermore, we train the LNN using stochastic gradient-based and state-estimation-based optimizers to learn the network's weights. The state-estimation-based method converges faster than the stochastic gradient-based method during neural network training. Simulation results are provided to illustrate the effectiveness of the proposed LNN-based Dyna framework for MBRL. |
| title | Integrating Lagrangian Neural Networks into the Dyna Framework for Reinforcement Learning |
| topic | Systems and Control Machine Learning |
| url | https://arxiv.org/abs/2603.08468 |