OPTIMIZING EXPECTATIONS: FROM DEEP REINFORCEMENT LEARNING TO STOCHASTIC COMPUTATION GRAPHS

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[Lev+16] [Lil+15] [Lin93] [MT03] [MS12] [Mar10] [MG14] [Mni+13] [Mni+14] [Mni+15] [Mni+16] [Mol+15] Bibliography 91 S. Levine, C. Finn, T. Darrell, and P. Abbeel. “End-to-end training of deep visuomotor policies.” In: Journal of Machine Learning Research 17.39 (2016), pp. 1–40 (cit. on pp. 3, 86). T. P. Lillicrap, J. J. Hunt, A. Pritzel, N. Heess, T. Erez, Y. Tassa, D. Silver, and D. Wierstra. “Continuous control with deep reinforcement learning.” In: arXiv preprint arXiv:1509.02971 (2015) (cit. on pp. 3, 5, 61, 62). L.-J. Lin. Reinforcement learning for robots using neural networks. Tech. rep. DTIC Docu- ment, 1993 (cit. on p. 2). P. Marbach and J. N. Tsitsiklis. “Approximate gradient methods in policy-space op- timization of Markov reward processes.” In: Discrete Event Dynamic Systems 13.1-2 (2003), pp. 111–148 (cit. on p. 47). J. Martens and I. Sutskever. “Training deep and recurrent networks with Hessian- free optimization.” In: Neural Networks: Tricks of the Trade. Springer, 2012, pp. 479– 535 (cit. on p. 40). J. Martens. “Deep learning via Hessian-free optimization.” In: Proceedings of the 27th International Conference on Machine Learning (ICML-10). 2010, pp. 735–742 (cit. on pp. 65, 73). A. Mnih and K. Gregor. “Neural variational inference and learning in belief net- works.” In: arXiv:1402.0030 (2014) (cit. on pp. 64, 76, 80, 81). V. Mnih, K. Kavukcuoglu, D. Silver, A. Graves, I. Antonoglou, D. Wierstra, and M. Riedmiller. “Playing Atari with Deep Reinforcement Learning.” In: arXiv preprint arXiv:1312.5602 (2013) (cit. on pp. 3, 5, 32, 33). V. Mnih, N. Heess, A. Graves, and K. Kavukcuoglu. “Recurrent models of visual attention.” In: Advances in Neural Information Processing Systems. 2014, pp. 2204–2212 (cit. on pp. 64, 83). V. Mnih, K. Kavukcuoglu, D. Silver, A. A. Rusu, J. Veness, M. G. Bellemare, A. Graves, M. Riedmiller, A. K. Fidjeland, G. Ostrovski, et al. “Human-level control through deep reinforcement learning.” In: Nature 518.7540 (2015), pp. 529–533 (cit. on p. 4). V. Mnih, A. P. Badia, M. Mirza, A. Graves, T. P. Lillicrap, T. Harley, D. Silver, and K. Kavukcuoglu. “Asynchronous methods for deep reinforcement learning.” In: arXiv preprint arXiv:1602.01783 (2016) (cit. on pp. 3, 17). T. M. Moldovan, S. Levine, M. I. Jordan, and P. Abbeel. “Optimism-driven explo- ration for nonlinear systems.” In: 2015 IEEE International Conference on Robotics and Automation (ICRA). IEEE. 2015, pp. 3239–3246 (cit. on p. 86).

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