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Recently, the use of transformers in offline reinforcement learning has become a rapidly developing area. This is due to their ability to treat the agent's trajectory in the environment as a sequence, thereby reducing the policy learning problem to s
Externí odkaz:
http://arxiv.org/abs/2306.09459
Autor:
Bessonov, Arkadii, Staroverov, Alexey, Zhang, Huzhenyu, Kovalev, Alexey K., Yudin, Dmitry, Panov, Aleksandr I.
Originally developed for natural language problems, transformer models have recently been widely used in offline reinforcement learning tasks. This is because the agent's history can be represented as a sequence, and the whole task can be reduced to
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::7904f4e65d3789773e7a7dc3c926c5c0