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Unsupervised exploration and representation learning become increasingly important when learning in diverse and sparse environments.
Empowerment: A universal agent-centric measure of control
Klyubin, A. S., D. Polani, and C. L. Nehaniv 2005 · 2005
Earlier work this paper cites.
Empowerment for continuous agent-environment systems
Jung, T., D. Polani, and P. Stone 2011 · 2011
Earlier work this paper cites.
Empowerment–an introduction
Salge, C., C. Glackin, and D. Polani 2014 · 2014
Earlier work this paper cites.
Variational information maximisation for intrinsically motivated reinforcement learning
Mohamed, S. and D. J. Rezende 2015 · 2015
Earlier work this paper cites.
Gregor, K., D. J. Rezende, and D. Wierstra 2016 · 2016
Cited alongside, same era.
When the map is better than the territory
Hoel, E. 2017 · 2017
Cited alongside, same era.
Proximal policy optimization algorithms
Schulman, J., F. Wolski, P. Dhariwal, A. Radford, and O. Klimov 2017 · 2017
Cited alongside, same era.
A unified bellman equation for causal information and value in markov decision processes
Tiomkin, S. and N. Tishby 2017 · 2017
Later among the works it cites.
Diversity is all you need: Learning skills without a reward function
Eysenbach, B., A. Gupta, J. Ibarz, and S. Levine 2018 · 2018
Later among the works it cites.
Disentangling the independently controllable factors of variation by interacting with the world
Thomas, V., E. Bengio, W. Fedus, J. Pondard, P. Beaudoin, H. Larochelle, J. Pineau, D. Precup, and Y. Bengio 2018 · 2018
Later among the works it cites.
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