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This work combines the free energy principle from cognitive neuroscience and the ensuing active inference dynamics with recent advances in variational inference in deep generative models, and evolution strategies to introduce the "deep active inference" agent.
Every good regulator of a system must be a model of that system
Conant, R. and Ashby, W. (1970) · 1970
Earlier work this paper cites.
Constrained differential optimization
Platt, J. C. and Barr, A. H. (1988) · 1988
Earlier work this paper cites.
Multilayer feedforward networks are universal approximators
Hornik, K., Stinchcombe, M., and White, H. (1989) · 1989
Earlier work this paper cites.
Variable resolution dynamic programming: Efficiently learning action maps in multivariate real-valued state-spaces
Moore, A. (1991) · 1991
Earlier work this paper cites.
Computation beyond the turing limit
Siegelmann, H. T. (1995) · 1995
Earlier work this paper cites.
Humans integrate visual and haptic information in a statistically optimal fashion
Ernst, M. and Banks, M. (2002) · 2002
Earlier work this paper cites.
The ventriloquist effect results from near-optimal bimodal integration
Alais, D. and Burr, D. (2004) · 2004
Earlier work this paper cites.
The bayesian brain: the role of uncertainty in neural coding and computation
Knill, D. and Pouget, A. (2004) · 2004
Earlier work this paper cites.
A theory of cortical responses
Friston, K. J. (2005) · 2005
Earlier work this paper cites.
A free energy principle for the brain
Friston, K. J., Kilner, J., and Harrison, L. (2006) · 2006
Earlier work this paper cites.
Reducing the dimensionality of data with neural networks
Hinton, G. E. and Salakhutdinov, R. R. (2006) · 2006
Earlier work this paper cites.
A recurrent network mechanism of time integration in perceptual decisions
Wong, K.-F. and Wang, X.-J. (2006) · 2006
Earlier work this paper cites.
Hierarchical models in the brain
Friston, K. J. (2008) · 2008
Earlier work this paper cites.
Predictive coding under the free-energy principle
Friston, K. J. and Kiebel, S. J. (2009) · 2009
Earlier work this paper cites.
The replicator equation as an inference dynamic
Harper, M. (2009) · 2009
Earlier work this paper cites.
The free-energy principle: a unified brain theory?
Friston, K. J. (2010) · 2010
Earlier work this paper cites.
Action and behavior: a free-energy formulation
Friston, K. J., Daunizeau, J., Kilner, J., and Kiebel, S. J. (2010) · 2010
Earlier work this paper cites.
Spontaneous cortical activity reveals hallmarks of an optimal internal model of the environment
Berkes, P., Orbán, G., Lengyel, M., and Fiser, J. (2011) · 2011
Cited alongside, same era.
Action understanding and active inference
Friston, K. J., Mattout, J., and Kilner, J. (2011) · 2011
Cited alongside, same era.
Bayesian sampling in visual perception
Moreno-Bote, R., Knill, D., and Pouget, A. (2011) · 2011
Cited alongside, same era.
Free-energy and illusions: the cornsweet effect
Brown, H. and Friston, K. J. (2012) · 2012
Cited alongside, same era.
A free energy principle for biological systems
Friston, K. J. (2012) · 2012
Cited alongside, same era.
Mujoco: A physics engine for model-based control
Todorov, E., Erez, T., and Tassa, Y. (2012) · 2012
Cited alongside, same era.
Deep learning
LeCun, Y., Bengio, Y., and Hinton, G. E. (2015) · 2015
Later among the works it cites.
Human-level control through deep reinforcement learning
Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A. A., Veness, J., Bellemare, M. G., Graves, A., Riedmiller, M., Fidjeland, A. K., Ostrovski, G., Petersen, S., Beattie, C., Sadik, A., Antonoglou, I., King, H., Kumaran, D., Wierstra, D., Legg, S., and Hassabis, D. (2015) · 2015
Later among the works it cites.
Variational inference with normalizing flows
Rezende, D. J. and Mohamed, S. (2015) · 2015
Later among the works it cites.
Evidence for surprise minimization over value maximization in choice behavior
Schwartenbeck, P., Fitzgerald, T., Mathys, C., Dolan, R., Kronbichler, M., and Friston, K. J. (2015) · 2015
Later among the works it cites.
Brockman, G., Cheung, V., Pettersson, L., Schneider, J., Schulman, J., Tang, J., and Zaremba, W. (2016) · 2016
Later among the works it cites.
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Life as we know it
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How can evolution learn?
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