পদ্ধতির তুলনা করুন

	বহুভাষিক রিইনফোর্সমেন্ট লার্নিং	রিইনফোর্সমেন্ট লার্নিং
ক্ষেত্র	গভীর শিখন	গভীর শিখন
পরিবার	Machine learning	Machine learning
উদ্ভবের বছর	2010s (applied to multilingual NLP settings)	1950s–1998
প্রবর্তক	Sutton, R. S. & Barto, A. G. (RL foundations); multilingual extensions emerged from the NLP/RL community in the 2010s	Sutton, R. S. & Barto, A. G. (formalised); Bellman, R. (foundations)
ধরন	Reinforcement learning applied to multilingual environments	Sequential decision-making framework
মৌলিক উৎস	Sutton, R. S., & Barto, A. G. (1998). Reinforcement Learning: An Introduction. MIT Press. ISBN: 978-0262193986	Sutton, R. S. & Barto, A. G. (2018). Reinforcement Learning: An Introduction (2nd ed.). MIT Press. ISBN: 978-0-262-03924-6
অপর নাম	Cross-Lingual RL, Multilingual RL, Multilingual Policy Learning, Cross-Lingual Reinforcement Learning	RL, reward-based learning, trial-and-error learning, policy optimization
সম্পর্কিত	5	2
সারসংক্ষেপ	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.	Reinforcement Learning (RL) is a framework in which an agent learns to make sequential decisions by interacting with an environment, receiving scalar reward signals, and updating a policy to maximise cumulative future reward. Unlike supervised learning, no labeled examples are provided; the agent discovers optimal behavior entirely through experience and delayed feedback.