The Sandbox Environment for Generalizable Agent Research (SEGAR)
Fuente:
arXiv
Salvato in:
| Autori principali: | , , , , , , , |
|---|---|
| Natura: | Preprint |
| Pubblicazione: |
2022
|
| Soggetti: | |
| Accesso online: | |
| Tags: |
Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
|
| _version_ | 1866914958110883840 |
|---|---|
| author | Hjelm, R Devon Mazoure, Bogdan Golemo, Florian Kahou, Samira Ebrahimi Braga, Pedro Frujeri, Felipe Jalobeanu, Mihai Kolobov, Andrey |
| author_facet | Hjelm, R Devon Mazoure, Bogdan Golemo, Florian Kahou, Samira Ebrahimi Braga, Pedro Frujeri, Felipe Jalobeanu, Mihai Kolobov, Andrey |
| contents | A broad challenge of research on generalization for sequential decision-making tasks in interactive environments is designing benchmarks that clearly landmark progress. While there has been notable headway, current benchmarks either do not provide suitable exposure nor intuitive control of the underlying factors, are not easy-to-implement, customizable, or extensible, or are computationally expensive to run. We built the Sandbox Environment for Generalizable Agent Research (SEGAR) with all of these things in mind. SEGAR improves the ease and accountability of generalization research in RL, as generalization objectives can be easy designed by specifying task distributions, which in turns allows the researcher to measure the nature of the generalization objective. We present an overview of SEGAR and how it contributes to these goals, as well as experiments that demonstrate a few types of research questions SEGAR can help answer. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2203_10351 |
| institution | arXiv |
| publishDate | 2022 |
| record_format | arxiv |
| spellingShingle | The Sandbox Environment for Generalizable Agent Research (SEGAR) Hjelm, R Devon Mazoure, Bogdan Golemo, Florian Kahou, Samira Ebrahimi Braga, Pedro Frujeri, Felipe Jalobeanu, Mihai Kolobov, Andrey Machine Learning A broad challenge of research on generalization for sequential decision-making tasks in interactive environments is designing benchmarks that clearly landmark progress. While there has been notable headway, current benchmarks either do not provide suitable exposure nor intuitive control of the underlying factors, are not easy-to-implement, customizable, or extensible, or are computationally expensive to run. We built the Sandbox Environment for Generalizable Agent Research (SEGAR) with all of these things in mind. SEGAR improves the ease and accountability of generalization research in RL, as generalization objectives can be easy designed by specifying task distributions, which in turns allows the researcher to measure the nature of the generalization objective. We present an overview of SEGAR and how it contributes to these goals, as well as experiments that demonstrate a few types of research questions SEGAR can help answer. |
| title | The Sandbox Environment for Generalizable Agent Research (SEGAR) |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2203.10351 |