পদ্ধতির তুলনা করুন

	মাল্টিমোডাল রিইনফোর্সমেন্ট লার্নিং	মাল্টিমোডাল গ্রাফ নিউরাল নেটওয়ার্ক
ক্ষেত্র	গভীর শিখন	গভীর শিখন
পরিবার	Machine learning	Machine learning
উদ্ভবের বছর	2015–2022	2019–2020
প্রবর্তক	Multiple contributors (DeepMind, OpenAI, Google Brain, 2010s–2020s)	Kipf & Welling (GNN foundation); extended to multimodal settings by multiple research groups c. 2019–2020
ধরন	Multimodal deep RL agent	Graph-based deep learning with multimodal input fusion
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.	Kipf, T. N., & Welling, M. (2017). Semi-Supervised Classification with Graph Convolutional Networks. International Conference on Learning Representations (ICLR).
অপর নাম	Multimodal RL, Multi-Sensory Reinforcement Learning, Vision-Language RL, Multi-Input RL	MM-GNN, Multimodal GNN, Multi-modal Graph Network, Cross-modal Graph Neural Network
সম্পর্কিত	6	6
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.	A Multimodal Graph Neural Network (MM-GNN) combines data from multiple modalities — such as text, images, and structured features — into a unified graph structure and applies graph-based message passing to learn joint representations. It enables relational reasoning across heterogeneous data sources, going beyond what unimodal or simple concatenation approaches can capture.