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Modern reinforcement learning algorithms can learn solutions to increasingly difficult control problems while at the same time reduce the amount of prior knowledge needed for their application.
Improving generalization for temporal difference learning: The successor representation
Peter Dayan · 1993
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Rich Caruana · 1997
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Policy invariance under reward transformations: Theory and application to reward shaping
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Reinforcement learning on explicitly specified time-scales
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All else being equal be empowered
Alexander S Klyubin, Daniel Polani, and Chrystopher L Nehaniv · 2005
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Where do rewards come from?
S. Singh, R. L. Lewis, and A. G. Barto · 2009
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Transfer learning for reinforcement learning domains: A survey
Matthew E Taylor and Peter Stone · 2009
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Richard S. Sutton, Joseph Modayil, Michael Delp, Thomas Degris, Patrick M. Pilarski, Adam White, and Doina Precup · 2011
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Learning skills from play: Artificial curiosity on a katana robot arm
Hung Quoc Ngo, Matthew D. Luciw, Alexander Förster, and Jürgen Schmidhuber · 2012
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Mujoco: A physics engine for model-based control
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Jürgen Schmidhuber · 2013
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Learning continuous control policies by stochastic value gradients
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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, Stig Petersen, Charles Beattie, Amir Sadik, Ioannis Antonoglou, Helen King, Dharshan Kumaran, Daan Wierstra, Shane Legg, and Demis Hassabis · 2015
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Variational information maximisation for intrinsically motivated reinforcement learning
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Intrinsically motivated goal exploration processes with automatic curriculum learning
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Automated curriculum learning for neural networks
Alex Graves, Marc G Bellemare, Jacob Menick, Remi Munos, and Koray Kavukcuoglu · 2017
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Variational intrinsic control
Karol Gregor, Danilo Jimenez Rezende, and Daan Wierstra · 2017
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Emergence of locomotion behaviours in rich environments
Nicolas Heess, Dhruva Tirumala, Srinivasan Sriram, Jay Lemmon, Josh Merel, Greg Wayne, Yuval Tassa, Tom Erez, Ziyu Wang, Ali Eslami, Martin Riedmiller, et al · 2017
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Universal Value function approximators
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Vime: Variational information maximizing exploration
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Mastering the game of Go with deep neural networks and tree search
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Max Jaderberg, V Mnih, W M Czarnecki, T Schaul, J Z Leibo, D Silver, and K Kavukcuoglu · 2017
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Maximum a posteriori policy optimisation
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Learning by playing solving sparse reward tasks from scratch
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Disentangled cumulants help successor representations transfer to new tasks
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Simultaneously learning vision and feature-based control policies for real-world ball-in-a-cup
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Rui Wang, Joel Lehman, Jeff Clune, and Kenneth O Stanley · 2019
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Regularized hierarchical policies for compositional transfer in robotics
Markus Wulfmeier, Abbas Abdolmaleki, Roland Hafner, Jost Tobias Springenberg, Michael Neunert, Tim Hertweck, Thomas Lampe, Noah Siegel, Nicolas Heess, and Martin Riedmiller · 2019
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Tracker motion capture software for vr object tracking, 2019
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