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| 다중 양식 강화학습 (Multimodal Reinforcement Learning)× | 다중 모달 트랜스포머× | |
|---|---|---|
| 분야 | 딥러닝 | 딥러닝 |
| 계열 | Machine learning | Machine learning |
| 기원 연도≠ | 2015–2022 | 2019–2021 |
| 창시자≠ | Multiple contributors (DeepMind, OpenAI, Google Brain, 2010s–2020s) | Lu et al. (ViLBERT); Radford et al. (CLIP) |
| 유형≠ | Multimodal deep RL agent | Cross-modal attention-based deep learning model |
| 원전≠ | 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 ↗ | Lu, J., Batra, D., Parikh, D., & Lee, S. (2019). ViLBERT: Pretraining Task-Agnostic Visiolinguistic Representations for Vision-and-Language Tasks. Advances in Neural Information Processing Systems (NeurIPS), 32. link ↗ |
| 별칭 | Multimodal RL, Multi-Sensory Reinforcement Learning, Vision-Language RL, Multi-Input RL | multimodal attention model, cross-modal transformer, vision-language transformer, multi-modal fusion transformer |
| 관련≠ | 6 | 5 |
| 요약≠ | 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 Transformer extends the standard Transformer architecture to process and jointly reason over two or more input modalities — most commonly text and images, but also audio, video, or structured data. Cross-modal attention layers allow information from one modality to inform representations in another, enabling tasks such as visual question answering, image captioning, and multimodal sentiment analysis. |
| ScholarGate데이터셋 ↗ |
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