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The central tenet of reinforcement learning (RL) is that agents seek to maximize the sum of cumulative rewards.
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, Afroz Mohiuddin, Ryan Sepassi, George Tucker, and Henryk Michalewski · 1903
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Bayesian policy selection using active inference
Ozan Catal, Johannes Nauta, Tim Verbelen, Pieter Simoens, and Bart Dhoedt · 1904
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A free energy principle for a particular physics
Karl Friston · 1906
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A free energy principle for a particular physics
Karl Friston · 1906
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Deep active inference as variational policy gradients
Beren Millidge · 1907
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Variational inference MPC for bayesian model-based reinforcement learning
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Alexander Tschantz, Manuel Baltieri, Anil K. Seth, and Christopher L. Buckley · 1911
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Deep ensembles: A loss landscape perspective
Stanislav Fort, Huiyi Hu, and Balaji Lakshminarayanan · 1912
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On a measure of the information provided by an experiment
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A survey of pomdp solution techniques: Theory
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A possibility for implementing curiosity and boredom in model-building neural controllers
Jürgen Schmidhuber · 1991
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Reinforcement driven information acquisition in non-deterministic environments
Jan Storck, Sepp Hochreiter, and Jürgen Schmidhuber · 1995
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Optimization of computer simulation models with rare events
Reuven Y Rubinstein · 1997
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Introduction to reinforcement learning , volume 135
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The bayesian brain: the role of uncertainty in neural coding and computation
David C Knill and Alexandre Pouget · 2004
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Intrinsically motivated reinforcement learning
Nuttapong Chentanez, Andrew G. Barto, and Satinder P. Singh · 2005
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Pattern recognition and machine learning
Christopher M Bishop · 2006
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Simple algorithmic principles of discovery, subjective beauty, selective attention, curiosity & creativity
Jürgen Schmidhuber · 2007
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Predictive coding under the free-energy principle
Karl Friston and Stefan Kiebel · 2008
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Bayesian models of cognition
Thomas L Griffiths, Charles Kemp, and Joshua B Tenenbaum · 2008
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Reinforcement learning or active inference?
Karl J Friston, Jean Daunizeau, and Stefan J Kiebel · 2009
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What is intrinsic motivation? a typology of computational approaches
Pierre-Yves Oudeyer and Frederic Kaplan · 2009
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Planning to be surprised: Optimal bayesian exploration in dynamic environments
Yi Sun, Faustino Gomez, and Juergen Schmidhuber · 2011
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Karl Friston, Spyridon Samothrakis, and Read Montague · 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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An information-theoretic approach to curiosity-driven reinforcement learning
Susanne Still and Doina Precup · 2012
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Auto-encoding variational bayes
Diederik P. Kingma and Max Welling · 2013
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On stochastic optimal control and reinforcement learning by approximate inference
Konrad Rawlik, Marc Toussaint, and Sethu Vijayakumar · 2013
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On probabilistic inference approaches to stochastic optimal control
Konrad Cyrus Rawlik · 2013
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Active inference in openai gym: A paradigm for computational investigations into psychiatric illness
Maell Cullen, Ben Davey, Karl J Friston, and Rosalyn J Moran · 2018
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A unified strategy for implementing curiosity and empowerment driven reinforcement learning
Ildefons Magrans de Abril and Ryota Kanai · 2018
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Deep temporal models and active inference
Karl J. Friston, Richard Rosch, Thomas Parr, Cathy Price, and Howard Bowman · 2018
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Recurrent world models facilitate policy evolution
David Ha and Jürgen Schmidhuber · 2018
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Soft actor-critic: Off-policy maximum entropy deep reinforcement learning with a stochastic actor
Tuomas Haarnoja, Aurick Zhou, Pieter Abbeel, and Sergey Levine · 2018
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Charles Blundell, Julien Cornebise, Koray Kavukcuoglu, and Daan Wierstra · 2015
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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 Riedmiller, Andreas K Fidjeland, Georg Ostrovski, et al · 2015
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Variational information maximisation for intrinsically motivated reinforcement learning
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Embed to control: A locally linear latent dynamics model for control from raw images
Manuel Watter, Jost Tobias Springenberg, Joschka Boedecker, and Martin Riedmiller · 2015
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Unifying count-based exploration and intrinsic motivation
Marc G. Bellemare, Sriram Srinivasan, Georg Ostrovski, Tom Schaul, David Saxton, and Remi Munos · 2016
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Learning latent dynamics for planning from pixels
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EMI: Exploration with mutual information
Hyoungseok Kim, Jaekyeom Kim, Yeonwoo Jeong, Sergey Levine, and Hyun Oh Song · 2018
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Reinforcement learning and control as probabilistic inference: Tutorial and review
Sergey Levine · 2018
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Approximate bayesian inference in spatial environments
Atanas Mirchev, Baris Kayalibay, Maximilian Soelch, Patrick van der Smagt, and Justin Bayer · 2018
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Neural network dynamics for model-based deep reinforcement learning with model-free fine-tuning
Anusha Nagabandi, Gregory Kahn, Ronald S Fearing, and Sergey Levine · 2018
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Hierarchical active inference: A theory of motivated control
Giovanni Pezzulo, Francesco Rigoli, and Karl J. Friston · 2018
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Pranav Shyam, Wojciech Jaśkowski, and Faustino Gomez · 2018
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An active learning perspective on exploration in reinforcement learning
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Variational inference for data-efficient model learning in POMDPs
Sebastian Tschiatschek, Kai Arulkumaran, Jan Stühmer, and Katja Hofmann · 2018
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Kai Ueltzhöffer · 2018
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Felix Leibfried, Sergio Pascual-Diaz, and Jordi Grau-Moya · 2019
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Deep dynamics models for learning dexterous manipulation
Anusha Nagabandi, Kurt Konoglie, Sergey Levine, and Vikash Kumar · 2019
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Neuronal message passing using mean-field, bethe, and marginal approximations
Thomas Parr, Dimitrije Markovic, Stefan J Kiebel, and Karl J Friston · 2019
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Noor Sajid, Philip J Ball, and Karl J Friston · 2019
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Model-based active exploration
Pranav Shyam, Wojciech Jaśkowski, and Faustino Gomez · 2019
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A distributional code for value in dopamine-based reinforcement learning
Will Dabney, Zeb Kurth-Nelson, Naoshige Uchida, Clara Kwon Starkweather, Demis Hassabis, Rémi Munos, and Matthew Botvinick · 2020
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Computational mechanisms of curiosity and goal-directed exploration
Philipp Schwartenbeck, Johannes Passecker, Tobias U Hauser, Thomas HB FitzGerald, Martin Kronbichler, and Karl J Friston · 2050
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