Deep variational reinforcement learning for POMDPs
Many real-world sequential decision making problems are partially observable by nature, and the environment model is typically unknown. Consequently, there is great need for reinforcement learning methods that can tackle such problems given only a stream of incomplete and noisy observations. In this...
| Auteurs principaux: | , , , , |
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| Format: | Conference item |
| Publié: |
Journal of Machine Learning Research
2018
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