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Learning to take actions based on observations is a core requirement for artificial agents to be able to be successful and robust at their task.
Aravind Srinivas, Allan Jabri, Pieter Abbeel, Sergey Levine, and Chelsea Finn · 1938
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Optimization of computer simulation models with rare events
Reuven Y. Rubinstein · 1996
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Algorithms for inverse reinforcement learning
Andrew Y. Ng and Stuart J. Russell · 2000
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A free energy principle for the brain
Karl Friston, James Kilner, and Lee Harrison · 2006
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The free-energy principle: A unified brain theory?
Karl Friston · 2010
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Artificial curiosity for autonomous space exploration
Vincent Graziano, Tobias Glasmachers, Tom Schaul, Leo Pape, Giuseppe Cuccu, Juxi Leitner, and Jürgen Schmidhuber · 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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The anatomy of choice: active inference and agency
Karl Friston, Philipp Schwartenbeck, Thomas FitzGerald, Michael Moutoussis, Timothy Behrens, and Raymond J. Dolan · 2013
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Auto-encoding variational bayes
Diederik P. Kingma and Max Welling · 2013
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Stochastic backpropagation and approximate inference in deep generative models
Danilo Jimenez Rezende, Shakir Mohamed, and Daan Wierstra · 2014
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Greg Brockman, Vicki Cheung, Ludwig Pettersson, Jonas Schneider, John Schulman, Jie Tang, and Wojciech Zaremba · 2016
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Mastering the game of Go with deep neural networks and tree search
David Silver, Aja Huang, Chris J. Maddison, Arthur Guez, Laurent Sifre, George Van Den Driessche, Julian Schrittwieser, Ioannis Antonoglou, Veda Panneershelvam, Marc Lanctot, Sander Dieleman, Dominik Grewe, John Nham, Nal Kalchbrenner, Ilya Sutskever, Timothy Lillicrap, Madeleine Leach, Koray Kavukcuoglu, Thore Graepel, and Demis Hassabis · 2016
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One-shot visual imitation learning via meta-learning
Chelsea Finn, Tianhe Yu, Tianhao Zhang, Pieter Abbeel, and Sergey Levine · 2017
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Active inference: A Process Theory
Karl Friston, Thomas FitzGerald, Francesco Rigoli, Philipp Schwartenbeck, and Giovanni Pezzulo · 2017
Evolution Strategies as a Scalable Alternative to Reinforcement Learning
Tim Salimans, Jonathan Ho, Xi Chen, Szymon Sidor, and Ilya Sutskever · 2017
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Collective robot reinforcement learning with distributed asynchronous guided policy search
Ali Yahya, Adrian Li, Mrinal Kalakrishnan, Yevgen Chebotar, and Sergey Levine · 2017
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David Ha and Jürgen Schmidhuber · 2018
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Qt-opt: Scalable deep reinforcement learning for vision-based robotic manipulation
Dmitry Kalashnikov, Alex Irpan, Peter Pastor, Julian Ibarz, Alexander Herzog, Eric Jang, Deirdre Quillen, Ethan Holly, Mrinal Kalakrishnan, Vincent Vanhoucke, and Sergey Levine · 2018
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Computational mechanisms of curiosity and goal-directed exploration
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Rainbow: Combining Improvements in Deep Reinforcement Learning
Matteo Hessel, Joseph Modayil, Hado van Hasselt, Tom Schaul, Georg Ostrovski, Will Dabney, Dan Horgan, Bilal Piot, Mohammad Azar, and David Silver · 2017
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A unified game-theoretic approach to multiagent reinforcement learning
Marc Lanctot, Vinícius Flores Zambaldi, Audrunas Gruslys, Angeliki Lazaridou, Karl Tuyls, Julien Pérolat, David Silver, and Thore Graepel · 2017
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Data-efficient deep reinforcement learning for dexterous manipulation
Ivaylo Popov, Nicolas Heess, Timothy P. Lillicrap, Roland Hafner, Gabriel Barth-Maron, Matej Vecerik, Thomas Lampe, Yuval Tassa, Tom Erez, and Martin A. Riedmiller · 2017
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Philipp Schwartenbeck, Johannes Passecker, Tobias Hauser, Thomas H B FitzGerald, Martin Kronbichler, and Karl J Friston · 2018
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Robot navigation using a variational dynamics model for state estimation and robust control
Sam Leroux Elias De Coninck Bert Vankeirsbilck Pieter Simoens Bart Dhoedt Steven Bohez, Tim Verbelen · 2018
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Unsupervised Predictive Memory in a Goal-Directed Agent
Greg Wayne, Chia-Chun Hung, David Amos, Mehdi Mirza, Arun Ahuja, Agnieszka Grabska-Barwinska, Jack Rae, Piotr Mirowski, Joel Z. Leibo, Adam Santoro, Mevlana Gemici, Malcolm Reynolds, Tim Harley, Josh Abramson, Shakir Mohamed, Danilo Rezende, David Saxton, Adam Cain, Chloe Hillier, David Silver, Koray Kavukcuoglu, Matt Botvinick, Demis Hassabis, and Timothy Lillicrap · 2018
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Towards sample efficient reinforcement learning
Yang Yu · 2018
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