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Active Inference is a theory of action arising from neuroscience which casts action and planning as a bayesian inference problem to be solved by minimizing a single quantity - the variational free energy.
Bayesian policy selection using active inference
Catal, O., Nauta, J., Verbelen, T., Simoens, P., and Dhoedt, B. (2019) · 1904
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A free energy principle for a particular physics
Friston, K. (2019) · 1906
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Reinforcement learning: A survey
Kaelbling, L. P., Littman, M. L., and Moore, A. W. (1996) · 1996
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Introduction to reinforcement learning
Sutton, R. S., Barto, A. G., et al. (1998) · 1998
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Predictive coding in the visual cortex: a functional interpretation of some extra-classical receptive-field effects
Rao, R. P. and Ballard, D. H. (1999) · 1999
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Policy gradient methods for reinforcement learning with function approximation
Sutton, R. S., McAllester, D. A., Singh, S. P., and Mansour, Y. (2000) · 2000
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Learning and inference in the brain
Friston, K. (2003) · 2003
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The bayesian brain: the role of uncertainty in neural coding and computation
Knill, D. C. and Pouget, A. (2004) · 2004
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A theory of cortical responses
Friston, K. (2005) · 2005
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Cortical substrates for exploratory decisions in humans
Daw, N. D., O’doherty, J. P., Dayan, P., Seymour, B., and Dolan, R. J. (2006) · 2006
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A free energy principle for the brain
Friston, K., Kilner, J., and Harrison, L. (2006) · 2006
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Bayesian brain: Probabilistic approaches to neural coding
Doya, K., Ishii, S., Pouget, A., and Rao, R. P. (2007) · 2007
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Free-energy and the brain
Friston, K. J. and Stephan, K. E. (2007) · 2007
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Maximum entropy inverse reinforcement learning
Ziebart, B. D., Maas, A. L., Bagnell, J. A., and Dey, A. K. (2008) · 2008
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The free-energy principle: a rough guide to the brain?
Friston, K. (2009) · 2009
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Reinforcement learning or active inference?
Friston, K. J., Daunizeau, J., and Kiebel, S. J. (2009) · 2009
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What is intrinsic motivation? a typology of computational approaches
Oudeyer, P.-Y. and Kaplan, F. (2009) · 2009
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Attention, uncertainty, and free-energy
Feldman, H. and Friston, K. (2010) · 2010
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The free-energy principle: a unified brain theory?
Friston, K. (2010) · 2010
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Approximate inference and stochastic optimal control
Rawlik, K., Toussaint, M., and Vijayakumar, S. (2010) · 2010
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Active inference, attention, and motor preparation
Brown, H., Friston, K. J., and Bestmann, S. (2011) · 2011
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Pilco: A model-based and data-efficient approach to policy search
Deisenroth, M. and Rasmussen, C. E. (2011) · 2011
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What is optimal about motor control?
Friston, K. (2011) · 2011
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Smooth pursuit and visual occlusion: active inference and oculomotor control in schizophrenia
Adams, R. A., Perrinet, L. U., and Friston, K. (2012) · 2012
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Planning as inference
Botvinick, M. and Toussaint, M. (2012) · 2012
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Dreaming the whole cat: Generative models, predictive processing, and the enactivist conception of perceptual experience
Clark, A. (2012) · 2012
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Degris, T., White, M., and Sutton, R. S. (2012) · 2012
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The history of the future of the bayesian brain
Friston, K. (2012) · 2012
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Free energy, value, and attractors
Friston, K. and Ao, P. (2012) · 2012
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Active inference and agency: optimal control without cost functions
Friston, K., Samothrakis, S., and Montague, R. (2012) · 2012
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A free energy principle for biological systems
Karl, F. (2012) · 2012
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Predictions not commands: active inference in the motor system
Adams, R. A., Shipp, S., and Friston, K. J. (2013) · 2013
Cited alongside, same era.
Whatever next? predictive brains, situated agents, and the future of cognitive science
Clark, A. (2013) · 2013
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The anatomy of choice: active inference and agency
Friston, K., Schwartenbeck, P., FitzGerald, T., Moutoussis, M., Behrens, T., and Dolan, R. J. (2013) · 2013
Cited alongside, same era.
Deep reinforcement learning with double q-learning
Van Hasselt, H., Guez, A., and Silver, D. (2016) · 2016
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Mean actor critic
Asadi, K., Allen, C., Roderick, M., Mohamed, A.-r., Konidaris, G., Littman, M., and Amazon, B. U. (2017) · 2017
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Variational inference: A review for statisticians
Blei, D. M., Kucukelbir, A., and McAuliffe, J. D. (2017) · 2017
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A tutorial on the free-energy framework for modelling perception and learning
Bogacz, R. (2017) · 2017
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The free energy principle for action and perception: A mathematical review
Buckley, C. L., Kim, C. S., McGregor, S., and Seth, A. K. (2017) · 2017
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Boltzmann exploration done right
Cesa-Bianchi, N., Gentile, C., Lugosi, G., and Neu, G. (2017) · 2017
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Stochastic variational inference
Hoffman, M. D., Blei, D. M., Wang, C., and Paisley, J. (2013) · 2013
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Auto-encoding variational bayes
Kingma, D. P. and Welling, M. (2013) · 2013
Cited alongside, same era.
Playing atari with deep reinforcement learning
Mnih, V., Kavukcuoglu, K., Silver, D., Graves, A., Antonoglou, I., Wierstra, D., and Riedmiller, M. (2013) · 2013
Cited alongside, same era.
On stochastic optimal control and reinforcement learning by approximate inference
Rawlik, K., Toussaint, M., and Vijayakumar, S. (2013) · 2013
Cited alongside, same era.
The anatomy of choice: dopamine and decision-making
Friston, K., Schwartenbeck, P., FitzGerald, T., Moutoussis, M., Behrens, T., and Dolan, R. J. (2014) · 2014
Cited alongside, same era.
Surfing uncertainty: Prediction, action, and the embodied mind
Clark, A. (2015) · 2015
Cited alongside, same era.
Reinforcement learning with deep energy-based policies
Haarnoja, T., Tang, H., Abbeel, P., and Levine, S. (2017) · 2017
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Uncertainty, epistemics and active inference
Parr, T. and Friston, K. J. (2017) · 2017
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Mastering the game of go without human knowledge
Silver, D., Schrittwieser, J., Simonyan, K., Antonoglou, I., Huang, A., Guez, A., Hubert, T., Baker, L., Lai, M., Bolton, A., et al. (2017) · 2017
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Expanding the active inference landscape: More intrinsic motivations in the perception-action loop
Biehl, M., Guckelsberger, C., Salge, C., Smith, S. C., and Polani, D. (2018) · 2018
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Expected policy gradients
Ciosek, K. and Whiteson, S. (2018) · 2018
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Virel: A variational inference framework for reinforcement learning
Fellows, M., Mahajan, A., Rudner, T. G., and Whiteson, S. (2018) · 2018
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Deep temporal models and active inference
Friston, K. J., Rosch, R., Parr, T., Price, C., and Bowman, H. (2018) · 2018
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Addressing function approximation error in actor-critic methods
Fujimoto, S., van Hoof, H., and Meger, D. (2018) · 2018
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Recurrent world models facilitate policy evolution
Ha, D. and Schmidhuber, J. (2018) · 2018
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Acquiring Diverse Robot Skills via Maximum Entropy Deep Reinforcement Learning
Haarnoja, T. (2018) · 2018
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Soft actor-critic: Off-policy maximum entropy deep reinforcement learning with a stochastic actor
Haarnoja, T., Zhou, A., Abbeel, P., and Levine, S. (2018) · 2018
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Reinforcement learning and control as probabilistic inference: Tutorial and review
Levine, S. (2018) · 2018
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Deep active inference
Ueltzhöffer, K. (2018) · 2018
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Unsupervised predictive memory in a goal-directed agent
Wayne, G., Hung, C.-C., Amos, D., Mirza, M., Ahuja, A., Grabska-Barwinska, A., Rae, J., Mirowski, P., Leibo, J. Z., Santoro, A., et al. (2018) · 2018
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Combining active inference and hierarchical predictive coding: A tutorial introduction and case study
Millidge, B. (2019) · 2019
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Impulsivity and active inference
Mirza, M. B., Adams, R. A., Parr, T., and Friston, K. (2019) · 2019
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Neuronal message passing using mean-field, bethe, and marginal approximations
Parr, T., Markovic, D., Kiebel, S. J., and Friston, K. J. (2019) · 2019
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Computational mechanisms of curiosity and goal-directed exploration
Schwartenbeck, P., Passecker, J., Hauser, T. U., FitzGerald, T. H., Kronbichler, M., and Friston, K. J. (2019) · 2019
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An active inference approach to modeling concept learning
Smith, R., Schwartenbeck, P., Parr, T., and Friston, K. J. (2019) · 2019
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Simulating active inference processes by message passing
van de Laar, T. W. and de Vries, B. (2019) · 2019
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Theories of error back-propagation in the brain
Whittington, J. C. and Bogacz, R. (2019) · 2019
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