The effectiveness of world models for continual reinforcement learning
World models power some of the most efficient reinforcement learning algorithms. In this work, we showcase that they can be harnessed for continual learning – a situation when the agent faces changing environments. World models typically employ a replay buffer for training, which can be naturally ex...
| Principais autores: | , , , , , , , |
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| Formato: | Conference item |
| Idioma: | English |
| Publicado em: |
Proceedings of Machine Learning Research
2023
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