Model-Based Reinforcement Learning with State Abstraction: A Survey

Author:

Starre Rolf A. N.,Loog Marco,Oliehoek Frans A.

Publisher

Springer Nature Switzerland

Reference83 articles.

1. Abel, D., Arumugam, D., Lehnert, L., Littman, M.: State abstractions for lifelong reinforcement learning. In: ICML (2018)

2. Abel, D., Hershkowitz, D., Littman, M.: Near optimal behavior via approximate state abstraction. In: ICML (2016)

3. Allen, C., Parikh, N., Gottesman, O., Konidaris, G.: Learning markov state abstractions for deep reinforcement learning. In: NeurIPS (2021)

4. Anand, A., Racah, E., Ozair, S., Bengio, Y., Côté, M.A., Hjelm, R.D.: Unsupervised state representation learning in atari. In: NeurIPS (2019)

5. Azizzadenesheli, K., Lazaric, A., Anandkumar, A.: Reinforcement learning in rich-observation mdps using spectral methods. arXiv (2016)

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