D. Amodei at al. Concrete problems in AI safety. arXiv:1606.06565, 2016. https://arxiv.org/abs/1606.06565
B. Baker, O. Gupta, N. Naik, and R. Raskar. Designing neural network architectures using reinforcement learning. arXiv:1611.02167, 2016. https://arxiv.org/abs/1611.02167
J. Baxter, A. Tridgell, and L. Weaver. Knightcap: a chess program that learns by combining td (lambda) with game-tree search. arXiv cs/9901002, 1999.
M. Bellemare, Y. Naddaf, J. Veness, and M. Bowling. The arcade learning environment: An evaluation platform for general agents. Journal of Artificial Intelligence Research, 47, pp. 253–279, 2013.
R. E. Bellman. Dynamic Programming. Princeton University Press, 1957.
M. Bojarski et al. End to end learning for self-driving cars. arXiv:1604.07316, 2016.https://arxiv.org/abs/1604.07316
M. Bojarski et al. Explaining How a Deep Neural Network Trained with End-to-End Learning Steers a Car. arXiv:1704.07911, 2017.https://arxiv.org/abs/1704.07911
C. Browne et al. A survey of monte carlo tree search methods. IEEE Transactions on Computational Intelligence and AI in Games, 4(1), pp. 1–43, 2012.
C. Clark and A. Storkey. Training deep convolutional neural networks to play go. ICML Confererence, pp. 1766–1774, 2015.
S. Gelly et al. The grand challenge of computer Go: Monte Carlo tree search and extensions. Communcations of the ACM, 55, pp. 106–113, 2012.
P. Glynn. Likelihood ratio gradient estimation: an overview, Proceedings of the 1987 Winter Simulation Conference, pp. 366–375, 1987.
I. Grondman, L. Busoniu, G. A. Lopes, and R. Babuska. A survey of actor-critic reinforcement learning: Standard and natural policy gradients. IEEE Transactions on Systems, Man, and Cybernetics, 42(6), pp. 1291–1307, 2012.
X. Guo, S. Singh, H. Lee, R. Lewis, and X. Wang. Deep learning for real-time Atari game play using offline Monte-Carlo tree search planning. Advances in NIPS Conference, pp. 3338–3346, 2014.
H. van Hasselt, A. Guez, and D. Silver. Deep Reinforcement Learning with Double Q-Learning. AAAI Conference, 2016.
N. Heess et al. Emergence of Locomotion Behaviours in Rich Environments. arXiv:1707.02286, 2017.https://arxiv.org/abs/1707.02286
Video 1 at:
Video 2 at:
S. Kakade. A natural policy gradient. NIPS Conference, pp. 1057–1063, 2002.
L. Kocsis and C. Szepesvari. Bandit based monte-carlo planning. ECML Conference, pp. 282–293, 2006.
M. Lai. Giraffe: Using deep reinforcement learning to play chess. arXiv:1509.01549, 2015.
S. Levine, C. Finn, T. Darrell, and P. Abbeel. End-to-end training of deep visuomotor policies. Journal of Machine Learning Research, 17(39), pp. 1–40, 2016.Video at:
M. Lewis, D. Yarats, Y. Dauphin, D. Parikh, and D. Batra. Deal or No Deal? End-to-End Learning for Negotiation Dialogues. arXiv:1706.05125, 2017.https://arxiv.org/abs/1706.05125
J. Li, W. Monroe, A. Ritter, M. Galley,, J. Gao, and D. Jurafsky. Deep reinforcement learning for dialogue generation. arXiv:1606.01541, 2016.https://arxiv.org/abs/1606.01541
Y. Li. Deep reinforcement learning: An overview. arXiv:1701.07274, 2017.https://arxiv.org/abs/1701.07274
L.-J. Lin. Reinforcement learning for robots using neural networks. Technical Report, DTIC Document, 1993.
C. Maddison, A. Huang, I. Sutskever, and D. Silver. Move evaluation in Go using deep convolutional neural networks. International Conference on Learning Representations, 2015.
V. Mnih et al. Human-level control through deep reinforcement learning. Nature, 518 (7540), pp. 529–533, 2015.
V. Mnih, K. Kavukcuoglu, D. Silver, A. Graves, I. Antonoglou, D. Wierstra, and M. Riedmiller. Playing atari with deep reinforcement learning. arXiv:1312.5602., 2013.https://arxiv.org/abs/1312.5602
V. Mnih et al. Asynchronous methods for deep reinforcement learning. ICML Confererence, pp. 1928–1937, 2016.
V. Mnih, N. Heess, and A. Graves. Recurrent models of visual attention. NIPS Conference, pp. 2204–2212, 2014.
A. Moore and C. Atkeson. Prioritized sweeping: Reinforcement learning with less data and less time. Machine Learning, 13(1), pp. 103–130, 1993.
M. Müller, M. Enzenberger, B. Arneson, and R. Segal. Fuego - an open-source framework for board games and Go engine based on Monte-Carlo tree search. IEEE Transactions on Computational Intelligence and AI in Games, 2, pp. 259–270, 2010.
K. S. Narendra and K. Parthasarathy. Identification and control of dynamical systems using neural networks. IEEE Transactions on Neural Networks, 1(1), pp. 4–27, 1990.
A. Ng and M. Jordan. PEGASUS: A policy search method for large MDPs and POMDPs. Uncertainity in Artificial Intelligence, pp. 406–415, 2000.
J. Peters and S. Schaal. Reinforcement learning of motor skills with policy gradients. Neural Networks, 21(4), pp. 682–697, 2008.
D. Pomerleau. ALVINN, an autonomous land vehicle in a neural network. Technical Report, Carnegie Mellon University, 1989.
G. Rummery and M. Niranjan. Online Q-learning using connectionist systems (Vol. 37). University of Cambridge, Department of Engineering, 1994.
A. Samuel. Some studies in machine learning using the game of checkers. IBM Journal of Research and Development, 3, pp. 210–229, 1959.
W. Saunders, G. Sastry, A. Stuhlmueller, and O. Evans. Trial without Error: Towards Safe Reinforcement Learning via Human Intervention. arXiv:1707.05173, 2017.https://arxiv.org/abs/1707.05173
S. Schaal. Is imitation learning the route to humanoid robots? Trends in Cognitive Sciences, 3(6), pp. 233–242, 1999.
T. Schaul, J. Quan, I. Antonoglou, and D. Silver. Prioritized experience replay. arXiv:1511.05952, 2015.https://arxiv.org/abs/1511.05952
J. Schulman, S. Levine, P. Abbeel, M. Jordan, and P. Moritz. Trust region policy optimization. ICML Conference, 2015.
J. Schulman, P. Moritz, S. Levine, M. Jordan, and P. Abbeel. High-dimensional continuous control using generalized advantage estimation. ICLR Conference, 2016.
I. Serban, A. Sordoni, Y. Bengio, A. Courville, and J. Pineau. Building end-to-end dialogue systems using generative hierarchical neural network models. AAAI Conference, pp. 3776–3784, 2016.
D. Silver et al. Mastering the game of Go with deep neural networks and tree search. Nature, 529.7587, pp. 484–489, 2016.
D. Silver et al. Mastering the game of go without human knowledge. Nature, 550.7676, pp. 354–359, 2017.
D. Silver et al. Mastering chess and shogi by self-play with a general reinforcement learning algorithm. arXiv, 2017.https://arxiv.org/abs/1712.01815
H. Simon. The Sciences of the Artificial. MIT Press, 1996.
I. Sutskever and V. Nair. Mimicking Go experts with convolutional neural networks. International Conference on Artificial Neural Networks, pp. 101–110, 2008.
R. Sutton. Learning to Predict by the Method of Temporal Differences, Machine Learning, 3, pp. 9–44, 1988.
R. Sutton and A. Barto. Reinforcement Learning: An Introduction. MIT Press, 1998.
R. Sutton, D. McAllester, S. Singh, and Y. Mansour. Policy gradient methods for reinforcement learning with function approximation. NIPS Conference, pp. 1057–1063, 2000.
G. Tesauro. Practical issues in temporal difference learning. Advances in NIPS Conference, pp. 259–266, 1992.
G. Tesauro. Td-gammon: A self-teaching backgammon program. Applications of Neural Networks, Springer, pp. 267–285, 1992.
G. Tesauro. Temporal difference learning and TD-Gammon. Communications of the ACM, 38(3), pp. 58–68, 1995.
S. Thrun. Learning to play the game of chess NIPS Conference, pp. 1069–1076, 1995.
Y. Tian, Q. Gong, W. Shang, Y. Wu, and L. Zitnick. ELF: An extensive, lightweight and flexible research platform for real-time strategy games. arXiv:1707.01067, 2017.https://arxiv.org/abs/1707.01067
O. Vinyals and Q. Le. A Neural Conversational Model. arXiv:1506.05869, 2015.https://arxiv.org/abs/1506.05869
C. J. H. Watkins. Learning from delayed rewards. PhD Thesis, King’s College, Cambridge, 1989.
C. J. H. Watkins and P. Dayan. Q-learning. Machine Learning, 8(3–4), pp. 279–292, 1992.
R. J. Williams. Simple statistical gradient-following algorithms for connectionist reinforcement learning. Machine Learning, 8(3–4), pp. 229–256, 1992.
K. Xu et al. Show, attend, and tell: Neural image caption generation with visual attention. ICML Confererence, 2015.
V. Zhong, C. Xiong, and R. Socher. Seq2SQL: Generating structured queries from natural language using reinforcement learning. arXiv:1709.00103, 2017.https://arxiv.org/abs/1709.00103
B. Zoph and Q. V. Le. Neural architecture search with reinforcement learning. arXiv:1611.01578, 2016.https://arxiv.org/abs/1611.01578