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| Multimodale Verstärkungslernverfahren (Multimodal Reinforcement Learning)× | Reinforcement Learning× | |
|---|---|---|
| Fachgebiet | Deep Learning | Deep Learning |
| Familie | Machine learning | Machine learning |
| Entstehungsjahr≠ | 2015–2022 | 1950s–1998 |
| Urheber≠ | Multiple contributors (DeepMind, OpenAI, Google Brain, 2010s–2020s) | Sutton, R. S. & Barto, A. G. (formalised); Bellman, R. (foundations) |
| Typ≠ | Multimodal deep RL agent | Sequential decision-making framework |
| Wegweisende Quelle≠ | 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. link ↗ | Sutton, R. S. & Barto, A. G. (2018). Reinforcement Learning: An Introduction (2nd ed.). MIT Press. ISBN: 978-0-262-03924-6 |
| Aliasnamen | Multimodal RL, Multi-Sensory Reinforcement Learning, Vision-Language RL, Multi-Input RL | RL, reward-based learning, trial-and-error learning, policy optimization |
| Verwandt≠ | 6 | 2 |
| Zusammenfassung≠ | 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. |
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