Memory Lens: How Much Memory Does an Agent Use?

Learning without state-estimation in partially observable Markovian decision processes

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Markov Decision Processes: Discrete Stochastic Dynamic Programming , volume 10

Martin L Puterman · 1995

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The Sciences of The Artifical

Herbert Simon · 1996

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Computational mechanics: Pattern and prediction, structure and simplicity

Cosma Rohilla Shalizi and James P. Crutchfield · 2001

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Entropy and inference, revisited

Ilya Nemenman, Fariel Shafee, and William Bialek · 2002

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Entropy Estimates from Insufficient Samplings

Peter Grassberger · 2003

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Variational information maximization for neural coding

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Felix Agakov and David Barber · 2004

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Entropy inference and the James-Stein estimator, with application to nonlinear gene association networks

Jean Hausser and Korbinian Strimmer · 2009

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Qualitative Analysis of Partially-Observable Markov Decision Processes

Krishnendu Chatterjee, Laurent Doyen, and Thomas A Henzinger · 2010

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Information Theory of Decisions and Actions

Naftali Tishby and Daniel Polani · 2011

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Improved information gain estimates for decision tree induction

Sebastian Nowozin · 2012

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Human-level control through deep reinforcement learning

Volodymyr Mnih, Koray Kavukcuoglu, David Silver, Andrei a Rusu, Joel Veness, Marc G Bellemare, Alex Graves, Martin Riedmiller, Andreas K Fidjeland, Georg Ostrovski, Stig Petersen, Charles Beattie, Amir Sadik, Ioannis Antonoglou, Helen King, Dharshan Kumaran, Daan Wierstra, Shane Legg, and Demis Hassabis · 2015

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Memory-based control with recurrent neural networks

Nicolas Heess, Jonathan J Hunt, Timothy P Lillicrap, and David Silver · 2015

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Recurrent Reinforcement Learning: A Hybrid Approach

Xiujun Li, Lihong Li, Jianfeng Gao, Xiaodong He, Jianshu Chen, Li Deng, and Ji He · 2015

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Contextual-MDPs for PAC-Reinforcement Learning with Rich Observations

Akshay Krishnamurthy, Alekh Agarwal, and John Langford · 2016

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Markov chain order estimation with conditional mutual information

M. Papapetrou and D. Kugiumtzis · 2016

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