Transformed Successor Features for Transfer Reinforcement Learning

Author:

Garces KiyoshigeORCID,Xuan JunyuORCID,Zuo HuaORCID

Publisher

Springer Nature Singapore

Reference20 articles.

1. Abdolshah, M., Le, H., George, T.K., Gupta, S., Rana, S., Venkatesh, S.: A new representation of successor features for transfer across dissimilar environments. In: International Conference on Machine Learning (ICML), vol. 139, pp. 1–9 (2021)

2. Abel, D., Arumugam, D., Lehnert, L., Littman, M.: State abstractions for lifelong reinforcement learning. In: Proceedings of the 35th International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 80 (2018)

3. Allen, C., Parikh, N., Gottesman, O., Konidaris, G.: Learning Markov state abstractions for deep reinforcement learning. Adv. Neural Inf. Process. Syst. 34, 8229–8241 (2021)

4. Barreto, A., et al.: Successor features for transfer in reinforcement learning. In: Advances in Neural Information Processing Systems (NIPS), vol. 30. Barcelona, Spain (2017)

5. Barreto, A., Hou, S., Borsa, D., Silver, D., Precup, D.: Fast reinforcement learning with generalized policy updates. Proc. Natl. Acad. Sci. 117, 30079–30087 (2020)

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