暂无分享,去创建一个
[1] Richard S. Sutton,et al. Reinforcement Learning: An Introduction , 1998, IEEE Trans. Neural Networks.
[2] Christopher D. Rosin,et al. Multi-armed bandits with episode context , 2011, Annals of Mathematics and Artificial Intelligence.
[3] Michael H. Bowling,et al. Action Selection for Hammer Shots in Curling , 2016, IJCAI.
[4] E. Nadaraya. On Estimating Regression , 1964 .
[5] Csaba Szepesvári,et al. Bandit Based Monte-Carlo Planning , 2006, ECML.
[6] Peter Auer,et al. Using Confidence Bounds for Exploitation-Exploration Trade-offs , 2003, J. Mach. Learn. Res..
[7] Demis Hassabis,et al. Mastering the game of Go without human knowledge , 2017, Nature.
[8] Rémi Coulom,et al. Computing "Elo Ratings" of Move Patterns in the Game of Go , 2007, J. Int. Comput. Games Assoc..
[9] Demis Hassabis,et al. Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm , 2017, ArXiv.
[10] Santiago Ontañón,et al. Combinatorial Multi-armed Bandits for Real-Time Strategy Games , 2017, J. Artif. Intell. Res..
[11] G. S. Watson,et al. Smooth regression analysis , 1964 .
[12] Demis Hassabis,et al. Mastering the game of Go with deep neural networks and tree search , 2016, Nature.
[13] H. Jaap van den Herik,et al. Progressive Strategies for Monte-Carlo Tree Search , 2008 .
[14] Nataliya Sokolovska,et al. Continuous Upper Confidence Trees , 2011, LION.
[15] Michael H. Bowling,et al. Monte Carlo Tree Search in Continuous Action Spaces with Execution Uncertainty , 2016, IJCAI.
[16] Jimmy Ba,et al. Adam: A Method for Stochastic Optimization , 2014, ICLR.
[17] Jaesik Choi,et al. Deep Reinforcement Learning in Continuous Action Spaces: a Case Study in the Game of Simulated Curling , 2018, ICML.
[18] Yoshua Bengio,et al. Understanding the difficulty of training deep feedforward neural networks , 2010, AISTATS.
[19] P. Cochat,et al. Et al , 2008, Archives de pediatrie : organe officiel de la Societe francaise de pediatrie.