I. Kostrikov, A. Nair, and S. Levine. Offline reinforcement learning with implicit q-learning. arXiv preprint arXiv:2110.06169, 2021.
S. Krishnamoorthy, S. M. Mashkaria, and A. Grover. Generative pretraining for black-box optimization. In International Conference on Machine Learning (ICML), 2023a.
S. Krishnamoorthy, S. M. Mashkaria, and A. Grover. Diffusion models for black-box optimization. In International Conference on Machine Learning (ICML), 2023b.
T. D. Kulkarni, K. Narasimhan, A. Saeedi, and J. Tenenbaum. Hierarchical deep reinforcement learning: Integrating temporal abstraction and intrinsic motivation. Advances in neural information processing systems, 29, 2016.
A. Kumar, J. Fu, M. Soh, G. Tucker, and S. Levine. Stabilizing off-policy q-learning via bootstrapping error reduction. Advances in Neural Information Processing Systems, 32, 2019.
A. Kumar, A. Zhou, G. Tucker, and S. Levine. Conservative q-learning for offline reinforcement learning. Advances in Neural Information Processing Systems, 33:1179-1191, 2020.
B.-J. Lee, J. Lee, and K.-E. Kim. Representation balancing offline model-based reinforcement learning. In International Conference on Learning Representations, 2021.
K.-H. Lee, O. Nachum, M. S. Yang, L. Lee, D. Freeman, S. Guadarrama, I. Fischer, W. Xu, E. Jang, H. Michalewski, et al. Multi-game decision transformers. Advances in Neural Information Processing Systems, 35:27921-27936, 2022.
S. Levine. Reinforcement learning and control as probabilistic inference: Tutorial and review. arXiv preprint arXiv:1805.00909, 2018.
F. Liu, H. Liu, A. Grover, and P. Abbeel. Masked autoencoding for scalable and generalizable decision making. In Advances in Neural Information Processing Systems (NeurIPS), 2022.
A. McGovern and A. G. Barto. Automatic discovery of subgoals in reinforcement learning using diverse density. In International Conference on Machine Learning (ICML), 2001.
J. A. Mendez, H. van Seijen, and E. EATON. Modular lifelong reinforcement learning via neural composition. In International Conference on Learning Representations, 2021.
D. Misra. Mish: A self regularized non-monotonic activation function. arXiv preprint arXiv:1908.08681, 2019.
S. A. Murphy, M. J. van der Laan, J. M. Robins, and C. P. P. R. Group. Marginal mean models for dynamic regimes. Journal of the American Statistical Association, 96(456):1410-1423, 2001.
T. Nguyen and A. Grover. Transformer neural processes: Uncertainty-aware meta learning via sequence modeling. In International Conference on Machine Learning (ICML), 2022.
T. Nguyen, Q. Zheng, and A. Grover. Reliable conditioning of behavioral cloning for offline reinforcement learning. arXiv preprint arXiv:2210.05158, 2022.
OpenAI. Openai: Introducing chatgpt, 2022. URL https://openai.com/blog/chatgpt.
M. L. Puterman. Markov decision processes: discrete stochastic dynamic programming. John Wiley & Sons, 2014.
A. Radford, K. Narasimhan, T. Salimans, I. Sutskever, et al. Improving language understanding by generative pre-training. In preprint, OpenAI, 2018.
R. Rafailov, T. Yu, A. Rajeswaran, and C. Finn. Offline reinforcement learning from images with latent space models. In Learning for Dynamics and Control, pages 1154-1168. PMLR, 2021.
S. Reed, K. Zolna, E. Parisotto, S. G. Colmenarejo, A. Novikov, G. Barth-Maron, M. Gimenez, Y. Sulsky, J. Kay, J. T. Springenberg, T. Eccles, J. Bruce, A. Razavi, A. Edwards, N. Heess, Y. Chen, R. Hadsell, O. Vinyals, M. Bordbar, and N. de Freitas. A generalist agent, 2022.