Abstraction Selection in Model-based Reinforcement Learning
暂无分享,去创建一个
[1] Yishay Mansour,et al. Policy Gradient Methods for Reinforcement Learning with Function Approximation , 1999, NIPS.
[2] Doina Precup,et al. Eligibility Traces for Off-Policy Policy Evaluation , 2000, ICML.
[3] Yishay Mansour,et al. Approximate Equivalence of Markov Decision Processes , 2003, COLT.
[4] Satinder Singh,et al. An upper bound on the loss from approximate optimal-value functions , 1994, Machine Learning.
[5] A. Barto,et al. An algebraic approach to abstraction in reinforcement learning , 2004 .
[6] Peter Stone,et al. State Abstraction Discovery from Irrelevant State Variables , 2005, IJCAI.
[7] Liming Xiang,et al. Kernel-Based Reinforcement Learning , 2006, ICIC.
[8] Thomas J. Walsh,et al. Towards a Unified Theory of State Abstraction for MDPs , 2006, AI&M.
[9] John N. Tsitsiklis,et al. Bias and Variance Approximation in Value Function Estimates , 2007, Manag. Sci..
[10] Lihong Li,et al. An analysis of linear models, linear value-function approximation, and feature selection for reinforcement learning , 2008, ICML '08.
[11] Andrew G. Barto,et al. Efficient skill learning using abstraction selection , 2009, IJCAI 2009.
[12] Monica Dinculescu,et al. Approximate Predictive Representations of Partially Observable Systems , 2010, ICML.
[13] Csaba Szepesvári,et al. Model Selection in Reinforcement Learning , 2011, Machine Learning.
[14] Eduardo F. Morales,et al. An Introduction to Reinforcement Learning , 2011 .
[15] Erik Talvitie,et al. Learning to Make Predictions In Partially Observable Environments Without a Generative Model , 2011, J. Artif. Intell. Res..
[16] Wei Chu,et al. Unbiased offline evaluation of contextual-bandit-based news article recommendation algorithms , 2010, WSDM '11.
[17] Guy Lever,et al. Modelling transition dynamics in MDPs with RKHS embeddings , 2012, ICML.
[18] Shie Mannor,et al. Model selection in markovian processes , 2013, KDD.
[19] Sergey Levine,et al. Offline policy evaluation across representations with applications to educational games , 2014, AAMAS.
[20] Shimon Whiteson,et al. EFFICIENT ABSTRACTION SELECTION IN REINFORCEMENT LEARNING , 2014, Comput. Intell..
[21] Ronald Ortner,et al. Selecting Near-Optimal Approximate State Representations in Reinforcement Learning , 2014, ALT.
[22] Shie Mannor,et al. How hard is my MDP?" The distribution-norm to the rescue" , 2014, NIPS.
[23] Andrea Lockerd Thomaz,et al. Abstraction from demonstration for efficient reinforcement learning in high-dimensional domains , 2014, Artif. Intell..
[24] Nan Jiang,et al. Improving UCT planning via approximate homomorphisms , 2014, AAMAS.
[25] Balaraman Ravindran. Approximate Homomorphisms : A framework for non-exact minimization in Markov Decision Processes , 2022 .