Employing Automatic Temporal Abstractions to Accelerate Utile Suffix Memory Algorithm
暂无分享,去创建一个
[1] Andrew McCallum,et al. Reinforcement learning with selective perception and hidden state , 1996 .
[2] Takeshi Yoshikawa,et al. An Acquiring Method of Macro-Actions in Reinforcement Learning , 2006, 2006 IEEE International Conference on Systems, Man and Cybernetics.
[3] Reda Alhajj,et al. Improving reinforcement learning by using sequence trees , 2010, Machine Learning.
[4] Lonnie Chrisman,et al. Reinforcement Learning with Perceptual Aliasing: The Perceptual Distinctions Approach , 1992, AAAI.
[5] Richard S. Sutton,et al. Introduction to Reinforcement Learning , 1998 .
[6] Andrew G. Barto,et al. Automatic Discovery of Subgoals in Reinforcement Learning using Diverse Density , 2001, ICML.
[7] Leslie Pack Kaelbling,et al. Learning Policies with External Memory , 1999, ICML.
[8] Doina Precup,et al. Between MDPs and Semi-MDPs: A Framework for Temporal Abstraction in Reinforcement Learning , 1999, Artif. Intell..
[9] Richard S. Sutton,et al. Reinforcement Learning: An Introduction , 1998, IEEE Trans. Neural Networks.
[10] Takashi Komeda,et al. REINFORCEMENT LEARNING FOR POMDP USING STATE CLASSIFICATION , 2008, MLMTA.
[11] Leslie Pack Kaelbling,et al. Learning Policies for Partially Observable Environments: Scaling Up , 1997, ICML.
[12] Jürgen Schmidhuber,et al. Long Short-Term Memory , 1997, Neural Computation.
[13] Faruk Polat,et al. Generating Memoryless Policies Faster Using Automatic Temporal Abstractions for Reinforcement Learning with Hidden State , 2013, 2013 IEEE 25th International Conference on Tools with Artificial Intelligence.
[14] Bernhard Hengst,et al. Discovering Hierarchy in Reinforcement Learning with HEXQ , 2002, ICML.