Compara mètodes

	Aprenentatge per Reforç Multilingüe	Transformer multilingüe
Camp	Aprenentatge profund	Aprenentatge profund
Família	Machine learning	Machine learning
Any d'origen	2010s (applied to multilingual NLP settings)	2019–2020
Autor original	Sutton, R. S. & Barto, A. G. (RL foundations); multilingual extensions emerged from the NLP/RL community in the 2010s	Devlin et al. (mBERT); Conneau et al. (XLM-R)
Tipus	Reinforcement learning applied to multilingual environments	Pre-trained cross-lingual language model
Font seminal	Sutton, R. S., & Barto, A. G. (1998). Reinforcement Learning: An Introduction. MIT Press. ISBN: 978-0262193986	Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2019). BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. Proceedings of NAACL-HLT 2019, pp. 4171–4186. Association for Computational Linguistics.
Àlies	Cross-Lingual RL, Multilingual RL, Multilingual Policy Learning, Cross-Lingual Reinforcement Learning	multilingual LM, cross-lingual transformer, mBERT-style model, multilingual pre-trained model
Relacionats	5	4
Resum	Multilingual Reinforcement Learning applies the RL paradigm — an agent learning by interaction and reward — to environments that involve multiple languages. The agent must interpret multilingual observations, follow cross-lingual instructions, or generalize policies trained in one language to new target languages, making it applicable to cross-lingual dialogue, multilingual game-playing agents, and language-grounded sequential decision tasks.	A multilingual transformer is a pre-trained language model built on the transformer architecture and trained jointly on text from dozens to over one hundred languages. Models such as mBERT and XLM-RoBERTa learn shared cross-lingual representations, enabling zero-shot or few-shot transfer: a model fine-tuned on English data can often be applied directly to French, German, Arabic, or Chinese without language-specific labels.