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| Học tăng cường đa phương thức× | Học chuyển giao với Học tăng cường× | |
|---|---|---|
| Lĩnh vực | Học sâu | Học sâu |
| Họ | Machine learning | Machine learning |
| Năm ra đời≠ | 2015–2022 | 2009 (survey); concept from early 2000s |
| Người khởi xướng≠ | Multiple contributors (DeepMind, OpenAI, Google Brain, 2010s–2020s) | Taylor, M. E. & Stone, P. |
| Loại≠ | Multimodal deep RL agent | Transfer learning paradigm for sequential decision-making |
| Công trình gốc≠ | 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 ↗ | Taylor, M. E., & Stone, P. (2009). Transfer Learning for Reinforcement Learning Domains: A Survey. Journal of Machine Learning Research, 10, 1633–1685. link ↗ |
| Tên gọi khác | Multimodal RL, Multi-Sensory Reinforcement Learning, Vision-Language RL, Multi-Input RL | Transfer RL, TL for RL, cross-task reinforcement learning, inductive transfer in RL |
| Liên quan≠ | 6 | 4 |
| Tóm tắt≠ | 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. | Transfer Learning with Reinforcement Learning (Transfer RL) is a training paradigm in which knowledge acquired by an agent in one or more source tasks — encoded as policy weights, value functions, or learned representations — is reused to accelerate or improve learning in a related but different target task. It directly addresses the sample-inefficiency that plagues reinforcement learning from scratch in complex or expensive environments. |
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