MAMBA: an Effective World Model Approach for Meta-Reinforcement Learning
Fuente:
arXiv
Salvato in:
| Autori principali: | , , , , |
|---|---|
| Natura: | Preprint |
| Pubblicazione: |
2024
|
| Soggetti: | |
| Accesso online: | |
| Tags: |
Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
|
| _version_ | 1866909137766449152 |
|---|---|
| author | Rimon, Zohar Jurgenson, Tom Krupnik, Orr Adler, Gilad Tamar, Aviv |
| author_facet | Rimon, Zohar Jurgenson, Tom Krupnik, Orr Adler, Gilad Tamar, Aviv |
| contents | Meta-reinforcement learning (meta-RL) is a promising framework for tackling challenging domains requiring efficient exploration. Existing meta-RL algorithms are characterized by low sample efficiency, and mostly focus on low-dimensional task distributions. In parallel, model-based RL methods have been successful in solving partially observable MDPs, of which meta-RL is a special case. In this work, we leverage this success and propose a new model-based approach to meta-RL, based on elements from existing state-of-the-art model-based and meta-RL methods. We demonstrate the effectiveness of our approach on common meta-RL benchmark domains, attaining greater return with better sample efficiency (up to $15\times$) while requiring very little hyperparameter tuning. In addition, we validate our approach on a slate of more challenging, higher-dimensional domains, taking a step towards real-world generalizing agents. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_09859 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | MAMBA: an Effective World Model Approach for Meta-Reinforcement Learning Rimon, Zohar Jurgenson, Tom Krupnik, Orr Adler, Gilad Tamar, Aviv Machine Learning Meta-reinforcement learning (meta-RL) is a promising framework for tackling challenging domains requiring efficient exploration. Existing meta-RL algorithms are characterized by low sample efficiency, and mostly focus on low-dimensional task distributions. In parallel, model-based RL methods have been successful in solving partially observable MDPs, of which meta-RL is a special case. In this work, we leverage this success and propose a new model-based approach to meta-RL, based on elements from existing state-of-the-art model-based and meta-RL methods. We demonstrate the effectiveness of our approach on common meta-RL benchmark domains, attaining greater return with better sample efficiency (up to $15\times$) while requiring very little hyperparameter tuning. In addition, we validate our approach on a slate of more challenging, higher-dimensional domains, taking a step towards real-world generalizing agents. |
| title | MAMBA: an Effective World Model Approach for Meta-Reinforcement Learning |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2403.09859 |