[42] KUMAR, A., ZHOU, A., TUCKER, G. and LEVINE, S. (2020). Conservative q-learning for offline reinforcement learning. Advances in Neural Information Processing Systems 33 1179-1191.
[43] KUMAR, P. and BECKER, A. (1982). A new family of optimal adaptive controllers for markov chains. IEEE Transactions on Automatic Control 27 137-146.
[44] KURUTACH, T., CLAVERA, I., DUAN, Y., TAMAR, A. and ABBEEL, P. (2018). Model-ensemble trust-region policy optimization. arXiv preprint arXiv:1802.10592.
[45] LEE, K., LASKIN, M., SRINIVAS, A. and ABBEEL, P. (2021). Sunrise: A simple unified framework for ensemble learning in deep reinforcement learning. In International Conference on Machine Learning. PMLR.
[46] LIU, Q., CHUNG, A., SZEPESVÁRÍ, C. and JIN, C. (2022). When is partially observable reinforcement learning not scary? arXiv preprint arXiv:2204.08967.
[47] LIU, Q., YU, T., BAI, Y. and JIN, C. (2020). A sharp analysis of model-based reinforcement learning with self-play. arXiv preprint arXiv:2010.01604.
[48] LIU, X., HSIEH, P.-C., HUNG, Y. H., BHATTACHARYA, A. and KUMAR, P. (2020). Exploration through reward biasing: Reward-biased maximum likelihood estimation for stochastic multi-armed bandits. In International Conference on Machine Learning. PMLR.
[49] LIU, Z., LU, M., WANG, Z., JORDAN, M. and YANG, Z. (2022). Welfare maximization in competitive equilibrium: Reinforcement learning for markov exchange economy. In International Conference on Machine Learning. PMLR.
[50] LIU, Z., ZHANG, Y., FU, Z., YANG, Z. and WANG, Z. (2022). Learning from demonstration: Provably efficient adversarial policy imitation with linear function approximation. In International Conference on Machine Learning. PMLR.
[51] LOPES, M., LANG, T., TOUSSAINT, M. and OUDEYER, P.-Y. (2012). Exploration in model-based reinforcement learning by empirically estimating learning progress. Advances in neural information processing systems 25.
[52] LU, M., MIN, Y., WANG, Z. and YANG, Z. (2022). Pessimism in the face of confounders: Provably efficient offline reinforcement learning in partially observable markov decision processes. arXiv preprint arXiv:2205.13589.
[53] LU, X. and VAN ROY, B. (2017). Ensemble sampling. Advances in neural information processing systems 30.
[54] METE, A., SINGH, R. and KUMAR, P. (2022). Augmented rbmle-ucb approach for adaptive control of linear quadratic systems. Advances in Neural Information Processing Systems 35 9302–9314.
[55] METE, A., SINGH, R. and KUMAR, P. (2022). The rbmle method for reinforcement learning. In 2022 56th Annual Conference on Information Sciences and Systems (CISS). IEEE.
[56] METE, A., SINGH, R., LIU, X. and KUMAR, P. (2021). Reward biased maximum likelihood estimation for reinforcement learning. In Learning for Dynamics and Control. PMLR.
[57] MODI, A., JIANG, N., TEWARI, A. and SINGH, S. (2020). Sample complexity of reinforcement learning using linearly combined model ensembles. In International Conference on Artificial Intelligence and Statistics. PMLR.
[58] MOHAMED, S. and JIMENEZ REZENDE, D. (2015). Variational information maximisation for intrinsically motivated reinforcement learning. Advances in neural information processing systems 28.
[59] OSBAND, I., BLUNDELL, C., PRITZEL, A. and VAN ROY, B. (2016). Deep exploration via bootstrapped dqn. Advances in neural information processing systems 29.