Linganisha mbinu

	Ujifunzaji Tumizi wa Njia Nyingi (Multimodal Reinforcement Learning)	Mtandao wa Neurali wa Mwingiliano wa Njia Nyingi
Nyanja	Ujifunzaji wa Kina	Ujifunzaji wa Kina
Familia	Machine learning	Machine learning
Mwaka wa asili	2015–2022	2019–2020
Mwanzilishi	Multiple contributors (DeepMind, OpenAI, Google Brain, 2010s–2020s)	Kipf & Welling (GNN foundation); extended to multimodal settings by multiple research groups c. 2019–2020
Aina	Multimodal deep RL agent	Graph-based deep learning with multimodal input fusion
Chanzo asilia	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).
Majina mbadala	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
Zinazohusiana	6	6
Muhtasari	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.