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Graph-based representations and message-passing modular policies constitute prominent approaches to tackling composable control problems in reinforcement learning (RL).
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Between mdps and semi-mdps: A framework for temporal abstraction in reinforcement learning
Richard S Sutton, Doina Precup, and Satinder Singh · 1999
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Completely derandomized self-adaptation in evolution strategies
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Recent advances in hierarchical reinforcement learning
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Evolution Strategies as a Scalable Alternative to Reinforcement Learning, September 2017
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A Vaswani · 2017
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Relational inductive biases, deep learning, and graph networks
Peter W Battaglia, Jessica B Hamrick, Victor Bapst, Alvaro Sanchez-Gonzalez, Vinicius Zambaldi, Mateusz Malinowski, Andrea Tacchetti, David Raposo, Adam Santoro, Ryan Faulkner, et al · 2018
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Jessica B Hamrick, Kelsey R Allen, Victor Bapst, Tina Zhu, Kevin R McKee, Joshua B Tenenbaum, and Peter W Battaglia · 2018
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Graph networks as learnable physics engines for inference and control
Alvaro Sanchez-Gonzalez, Nicolas Heess, Jost Tobias Springenberg, Josh Merel, Martin Riedmiller, Raia Hadsell, and Peter Battaglia · 2018
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Reinforcement learning: an introduction
Richard S. Sutton and Andrew G. Barto · 2018
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Magnetic control of tokamak plasmas through deep reinforcement learning
Jonas Degrave, Federico Felici, Jonas Buchli, Michael Neunert, Brendan Tracey, Francesco Carpanese, Timo Ewalds, Roland Hafner, Abbas Abdolmaleki, Diego de Las Casas, et al · 2022
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Graph-based reinforcement learning meets mixed integer programs: An application to 3d robot assembly discovery
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Understanding pooling in graph neural networks
Daniele Grattarola, Daniele Zambon, Filippo Maria Bianchi, and Cesare Alippi · 2022
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Deep reinforcement learning with relational inductive biases
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Spectral clustering with graph neural networks for graph pooling
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One policy to control them all: Shared modular policies for agent-agnostic control
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Structural entropy guided graph hierarchical pooling
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Outracing champion gran turismo drivers with deep reinforcement learning
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Subequivariant graph reinforcement learning in 3d environments
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