[38] Dohyeong Kim and Songhwai Oh. Efficient off-policy safe reinforcement learning using trust region conditional value at risk. IEEE Robotics and Automation Letters, 7(3):7644-7651, 2022.
[39] Zhuangdi Zhu, Kaixiang Lin, Anil K Jain, and Jiayu Zhou. Transfer learning in deep reinforcement learning: A survey. arXiv preprint arXiv:2009.07888, 2020.
[40] Pashootan Vaezipoor, Andrew C Li, Rodrigo A Toro Icarte, and Sheila A Mcilraith. Ltl2action: Generalizing ltl instructions for multi-task rl. In International Conference on Machine Learning, pages 10497-10508. PMLR, 2021.
[41] Brandon Araki, Xiao Li, Kiran Vodrahalli, Jonathan DeCastro, Micah Fry, and Daniela Rus. The logical options framework. In International Conference on Machine Learning, pages 307-317. PMLR, 2021.
[42] Emanuel Todorov. Compositionality of optimal control laws. Advances in neural information processing systems, 22, 2009.
[43] Geraud Nangue Tasse, Steven James, and Benjamin Rosman. A boolean task algebra for reinforcement learning. Advances in Neural Information Processing Systems, 33:9497-9507, 2020.
[44] Janarthanan Rajendran, Aravind Srinivas, Mitesh M Khapra, P Prasanna, and Balaraman Ravindran. Attend, adapt and transfer: Attentive deep architecture for adaptive transfer from multiple sources in the same domain. arXiv preprint arXiv:1510.02879, 2015.
[45] Chen Ma, Dylan R Ashley, Junfeng Wen, and Yoshua Bengio. Universal successor features for transfer reinforcement learning. arXiv preprint arXiv:2001.04025, 2020.
[46] Jaekyeom Kim, Seohong Park, and Gunhee Kim. Constrained gpi for zero-shot transfer in reinforcement learning. Advances in Neural Information Processing Systems, 35:4585-4597, 2022.
[47] Mark Nemecek and Ronald Parr. Policy caches with successor features. In International Conference on Machine Learning, pages 8025-8033. PMLR, 2021.
[48] Peter Dayan. Improving generalization for temporal difference learning: The successor representation. Neural computation, 5(4):613-624, 1993.
[49] Ida Momennejad, Evan M Russek, Jin H Cheong, Matthew M Botvinick, Nathaniel Douglass Daw, and Samuel J Gershman. The successor representation in human reinforcement learning. Nature human behaviour, 1(9):680-692, 2017.
[50] Eitan Altman. Constrained Markov decision processes: stochastic modeling. Routledge, 1999.
[51] Yoshua Bengio, Aaron Courville, and Pascal Vincent. Representation learning: A review and new perspectives. IEEE transactions on pattern analysis and machine intelligence, 35(8):1798-1828, 2013.
[52] Sergey Levine. Reinforcement learning and control as probabilistic inference: Tutorial and review. arXiv preprint arXiv:1805.00909, 2018.
[53] Zuxin Liu, Zijian Guo, Zhepeng Cen, Huan Zhang, Yihang Yao, Hanjiang Hu, and Ding Zhao. Towards robust and safe reinforcement learning with benign off-policy data. 2023.
[54] Abbas Abdolmaleki, Jost Tobias Springenberg, Yuval Tassa, Remi Munos, Nicolas Heess, and Martin Riedmiller. Maximum a posteriori policy optimisation. arXiv preprint arXiv:1806.06920, 2018.
[55] Abbas Abdelmaleki, Jost Tobias Springenberg, Jonas Degrave, Steven Bohez, Yuval Tassa, Dan Belov, Nicolas Heess, and Martin Riedmiller. Relative entropy regularized policy iteration. arXiv preprint arXiv:1812.02256, 2018.
[56] Sven Gronauer. Bullet-safety-gym: A framework for constrained reinforcement learning. 2022.