方法证据记录
Multimodal Reinforcement Learning
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.
源记录
引文逐字复制自方法源记录。这些引文不代表任何层级的验证。
Multimodal Reinforcement Learning (Multi-Sensory RL Agent Learning)
分类方法记录 · ml-model / deep-learning
- 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. · URL
- Multimodal learning. Wikipedia. · URL
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