Clavera, I., Rothfuss, J., Schulman, J., Fujita, Y., Asfour, T., and Abbeel, P. (2018). Model-based reinforcement learning via meta-policy optimization. CoRR, abs/1809.05214.
Cohen, T. and Welling, M. (2016a). Group equivariant convolutional networks. In Int. Conf. Machine Learning (ICML), pages 2990-2999. PMLR.
Cohen, T. S. and Welling, M. (2016b). Steerable cnns. arXiv preprint arXiv:1612.08498.
Dai, J. G. and Gluzman, M. (2022). Queueing network controls via deep reinforcement learning. Stochastic Systems, 12(1):30-67.
Du, S., Krishnamurthy, A., Jiang, N., Agarwal, A., Dudik, M., and Langford, J. (2019). Provably efficient rl with rich observations via latent state decoding. In Int. Conf. Machine Learning (ICML), pages 1665-1674. PMLR.
Ernst, D., Geurts, P., and Wehenkel, L. (2005). Tree-based batch mode reinforcement learning. Journal of Machine Learning Research, 6:503-556.
Fan, L., Wang, G., Huang, D.-A., Yu, Z., Fei-Fei, L., Zhu, Y., and Anandkumar, A. (2021). SECANT: self-expert cloning for zero-shot generalization of visual policies. In Int. Conf. Machine Learning (ICML).
Fu, L., Fu, X., Zhang, Z., Xu, Z., Wu, X., Wang, X., and Lu, S. (2018). Joint optimization of multicast energy in delay-constrained mobile wireless networks. IEEE/ACM Transactions on Networking, 26(1):633-646.
Haarnoja, T., Zhou, A., Abbeel, P., and Levine, S. (2018). Soft Actor-Critic: Off-policy maximum entropy deep reinforcement learning with a stochastic actor. In Int. Conf. Machine Learning (ICML), pages 1861-1870.
Janner, M., Fu, J., Zhang, M., and Levine, S. (2019). When to trust your model: Model-based policy optimization. In Advances Neural Information Processing Systems (NeurIPS), volume 32.
Kakade, S. and Langford, J. (2002). Approximately optimal approximate reinforcement learning. In Int. Conf. Machine Learning (ICML), pages 267-274.
Kearns, M. and Koller, D. (1999). Efficient reinforcement learning in factored mdps. In Proc. Int. Joint Conf. Artif. Intell. Org (IJCAI), volume 16, pages 740-747.
Kurutach, T., Clavera, I., Duan, Y., Tamar, A., and Abbeel, P. (2018). Model-ensemble trust-region policy optimization. arXiv preprint arXiv:1802.10592.
Laskin, M., Lee, K., Stoke, A., Pinto, L., Abbeel, P., and Srinivas, A. (2020). Reinforcement learning with augmented data. In Advances Neural Information Processing Systems (NeurIPS). Curran Associates Inc.
Lin, Y., Huang, J., Zimmer, M., Guan, Y., Rojas, J., and Weng, P. (2020). Invariant transform experience replay: Data augmentation for deep reinforcement learning. IEEE Robotics and Automation Letters, 5(4):6615-6622.
Luo, Y., Xu, H., Li, Y., Tian, Y., Darrell, T., and Ma, T. (2019). Algorithmic framework for model-based deep reinforcement learning with theoretical guarantees. In Int. Conf. on Learning Representations (ICLR).
Machado, M. C., Bellemare, M. G., and Bowling, M. (2020). Count-based exploration with the successor representation. In AAAI Conf. Artificial Intelligence, pages 5125-5133.
Misra, D., Henaff, M., Krishnamurthy, A., and Langford, J. (2020). Kinematic state abstraction and provably efficient rich-observation reinforcement learning. In Int. Conf. Machine Learning (ICML), pages 6961-6971. PMLR.
Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A. A., Veness, J., Bellemare, M. G., Graves, A., Riedmiller, M., Fidjeland, A. K., and Ostrovski, G. (2015a). Human-level control through deep reinforcement learning. Nature, 518(7540):529-533.