Attentive Multi-task Deep Reinforcement Learning

Author:

Bräm Timo,Brunner GinoORCID,Richter OliverORCID,Wattenhofer Roger

Publisher

Springer International Publishing

Reference31 articles.

1. Aytar, Y., Pfaff, T., Budden, D., Paine, T.L., Wang, Z., de Freitas, N.: Playing hard exploration games by watching Youtube. CoRR abs/1805.11592 (2018). http://arxiv.org/abs/1805.11592

2. Barreto, A., et al.: Transfer in deep reinforcement learning using successor features and generalised policy improvement. In: Proceedings of the 35th International Conference on Machine Learning, ICML 2018 (2018). http://proceedings.mlr.press/v80/barreto18a.html

3. Barreto, A., et al.: Successor features for transfer in reinforcement learning. In: Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017 (2017). http://papers.nips.cc/paper/6994-successor-features-for-transfer-in-reinforcement-learning

4. Birck, M., Corrêa, U., Ballester, P., Andersson, V., Araujo, R.: Multi-task reinforcement learning: an hybrid A3C domain approach, January 2017

5. Czarnecki, W.M., et al.: Mix&match-agent curricula for reinforcement learning. arXiv preprint arXiv:1806.01780 (2018)

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