Σύγκριση μεθόδων

	Πολυτροπική Ενισχυτική Μάθηση	Ενισχυτική Μάθηση
Πεδίο	Βαθιά Μάθηση	Βαθιά Μάθηση
Οικογένεια	Machine learning	Machine learning
Έτος προέλευσης	2015–2022	1950s–1998
Δημιουργός	Multiple contributors (DeepMind, OpenAI, Google Brain, 2010s–2020s)	Sutton, R. S. & Barto, A. G. (formalised); Bellman, R. (foundations)
Τύπος	Multimodal deep RL agent	Sequential decision-making framework
Θεμελιώδης πηγή	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
Εναλλακτικές ονομασίες	Multimodal RL, Multi-Sensory Reinforcement Learning, Vision-Language RL, Multi-Input RL	RL, reward-based learning, trial-and-error learning, policy optimization
Συναφείς	6	2
Σύνοψη	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.