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Active inference may be defined as Bayesian modeling of a brain with a biologically plausible model of the agent.
Dynamic Programming
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Alvinn: An autonomous land vehicle in a neural network
Dean A Pomerleau · 1989
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Q-learning
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Gavin A Rummery and Mahesan Niranjan · 1994
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Policy gradient methods for reinforcement learning with function approximation
Richard S. Sutton, David A. McAllester, Satinder P. Singh, and Yishay Mansour · 1999
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A free energy principle for the brain
K. Friston, J. Kilner, and L. Harrison · 2006
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General duality between optimal control and estimation
Emanuel Todorov · 2008
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Maximum entropy inverse reinforcement learning
Brian D Ziebart, Andrew L Maas, J Andrew Bagnell, and Anind K Dey · 2008
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Reinforcement learning or active inference?
Karl J Friston, Jean Daunizeau, and Stefan J Kiebel · 2009
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Optimal control as a graphical model inference problem
Bert Kappen, Vicenç Gómez, and Manfred Opper · 2009
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A theoretical and empirical analysis of expected sarsa
Harm Van Seijen, Hado Van Hasselt, Shimon Whiteson, and Marco Wiering · 2009
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The free-energy principle: a unified brain theory?
Karl Friston · 2010
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Double q-learning
Hado Hasselt · 2010
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Action understanding and active inference
Karl J. Friston, Jérémie Mattout, and James Kilner · 2011
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Active inference and agency: Optimal control without cost functions
Karl Friston, Spyridon Samothrakis, and Read Montague · 2012
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A free energy principle for biological systems
Karl J. Friston · 2012
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Optimal control as a graphical model inference problem
Hilbert J Kappen, Vicenç Gómez, and Manfred Opper · 2012
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The anatomy of choice: active inference and agency
Karl Friston, Philipp Schwartenbeck, Thomas Fitzgerald, Michael Moutoussis, Tim Behrens, and Raymond Dolan · 2013
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Playing atari with deep reinforcement learning
Volodymyr Mnih, Koray Kavukcuoglu, David Silver, Alex Graves, Ioannis Antonoglou, Daan Wierstra, and Martin A. Riedmiller · 2013
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On probabilistic inference approaches to stochastic optimal control
Konrad Cyrus Rawlik · 2013
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Active inference and epistemic value
Karl Friston, Francesco Rigoli, Dimitri Ognibene, Christoph Mathys, Thomas Fitzgerald, and Giovanni Pezzulo · 2015
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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 A. Riedmiller, Andreas 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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Openai gym, 2016
Greg Brockman, Vicki Cheung, Ludwig Pettersson, Jonas Schneider, John Schulman, Jie Tang, and Wojciech Zaremba · 2016
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Deep variational reinforcement learning for pomdps
Maximilian Igl, Luisa Zintgraf, Tuan Anh Le, Frank Wood, and Shimon Whiteson · 2018
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Reinforcement learning and control as probabilistic inference: Tutorial and review
Sergey Levine · 2018
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Reinforcement learning and control as probabilistic inference: Tutorial and review
Sergey Levine · 2018
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Rl baselines zoo
Antonin Raffin · 2018
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Reinforcement learning: An introduction
Richard S Sutton and Andrew G Barto · 2018
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How prior preferences determine decision-making frames and biases in the human brain
Alizée Lopez-Persem, Philippe Domenech, and Mathias Pessiglione · 2016
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Asynchronous methods for deep reinforcement learning
Volodymyr Mnih, Adrià Puigdomènech Badia, Mehdi Mirza, Alex Graves, Timothy P. Lillicrap, Tim Harley, David Silver, and Koray Kavukcuoglu · 2016
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Bat q-learning algorithm
Bilal H Abed-alguni · 2017
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Hindsight experience replay
Marcin Andrychowicz, Dwight Crow, Alex Ray, Jonas Schneider, Rachel Fong, Peter Welinder, Bob McGrew, Josh Tobin, Pieter Abbeel, and Wojciech Zaremba · 2017
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A distributional perspective on reinforcement learning
Marc G Bellemare, Will Dabney, and Rémi Munos · 2017
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Zap q-learning
Adithya M Devraj and Sean P Meyn · 2017
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Active inference: A process theory
Karl Friston, Thomas FitzGerald, Francesco Rigoli, Philipp Schwartenbeck, and Giovanni Pezzulo · 2017
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Kai Ueltzhöffer · 2018
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A free energy principle for a particular physics, 2019
Karl J. Friston · 2019
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Model-based reinforcement learning for atari
Lukasz Kaiser, Mohammad Babaeizadeh, Piotr Milos, Blazej Osinski, Roy H Campbell, Konrad Czechowski, Dumitru Erhan, Chelsea Finn, Piotr Kozakowski, Sergey Levine, et al · 2019
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Efficient exploration via state marginal matching
Lisa Lee, Benjamin Eysenbach, Emilio Parisotto, Eric Xing, Sergey Levine, and Ruslan Salakhutdinov · 2019
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Generalised free energy and active inference
Thomas Parr and Karl J. Friston · 2019
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Alexander Tschantz, Manuel Baltieri, Anil K. Seth, and Christopher L. Buckley · 2019
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Deep active inference agents using monte-carlo methods
Zafeirios Fountas, Noor Sajid, Pedro A. M. Mediano, and Karl J. Friston · 2020
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Deep active inference as variational policy gradients
Beren Millidge · 2020
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Whence the expected free energy?
Beren Millidge, Alexander Tschantz, and Christopher L. Buckley · 2020
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Reinforcement learning through active inference
Alexander Tschantz, Beren Millidge, Anil K. Seth, and Christopher L. Buckley · 2020
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Learning perception and planning with deep active inference
O. Çatal, T. Verbelen, J. Nauta, C. D. Boom, and B. Dhoedt · 2020
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Active inference: Demystified and compared
Noor Sajid, Philip J. Ball, Thomas Parr, and Karl J. Friston · 2021
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