Multimodal information bottleneck for deep reinforcement learning with multiple sensors

Author:

You BangORCID,Liu HuapingORCID

Funder

National Natural Science Foundation of China

Publisher

Elsevier BV

Reference50 articles.

1. Alemi, A. A., Fischer, I., Dillon, J. V., & Murphy, K. (2017). Deep Variational Information Bottleneck. In International conference on learning representations.

2. Anand, A., Racah, E., Ozair, S., Bengio, Y., Côté, M.-A., & Hjelm, R. D. (2019). Unsupervised state representation learning in atari. In Proceedings of the 33rd international conference on neural information processing systems (pp. 8769–8782).

3. Dynamic bottleneck for robust self-supervised exploration;Bai;Advances in Neural Information Processing Systems,2021

4. Reinforcement learning from multiple sensors via joint representations;Becker,2023

5. Multi-modal mutual information (mummi) training for robust self-supervised deep reinforcement learning;Chen,2021

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