পদ্ধতির তুলনা করুন

	মাল্টিমোডাল রিইনফোর্সমেন্ট লার্নিং	রিইনফোর্সমেন্ট লার্নিং
ক্ষেত্র	গভীর শিখন	গভীর শিখন
পরিবার	Machine learning	Machine learning
উদ্ভবের বছর	2015–2022	1950s–1998
প্রবর্তক	Multiple contributors (DeepMind, OpenAI, Google Brain, 2010s–2020s)	Sutton, R. S. & Barto, A. G. (formalised); Bellman, R. (foundations)
ধরন	Multimodal deep RL agent	Sequential decision-making framework
মৌলিক উৎস	Reed, S., Zolna, K., Parisotto, E., Colmenarejo, S. G., Novikov, A., Barth-Maron, G., ... & de Freitas, N. (2022). A Generalist Agent. Transactions on Machine Learning Research.	Sutton, R. S. & Barto, A. G. (2018). Reinforcement Learning: An Introduction (2nd ed.). MIT Press. ISBN: 978-0-262-03924-6
অপর নাম	Multimodal RL, Multi-Sensory Reinforcement Learning, Vision-Language RL, Multi-Input RL	RL, reward-based learning, trial-and-error learning, policy optimization
সম্পর্কিত	6	2
সারসংক্ষেপ	Multimodal Reinforcement Learning trains agents to make sequential decisions by perceiving and integrating multiple input modalities — such as raw pixels, language instructions, audio, and proprioceptive sensors — simultaneously. Rather than acting on a single data stream, the agent fuses heterogeneous signals into a unified state representation and learns a policy through environmental reward feedback.	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.