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The ability to learn good representations of states is essential for solving large reinforcement learning problems, where exploration, generalization, and transfer are particularly challenging.
Improving Generalization for Temporal Difference Learning: The Successor Representation
Peter Dayan · 1993
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Yehuda Koren · 2003
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Proto-value Functions: Developmental Reinforcement Learning
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Ordinary Differential Equations with Applications
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Numerical Optimization
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Proto-value Functions: A Laplacian Framework for Learning Representation and Control in Markov Decision Processes
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An Analysis of Laplacian Methods for Value Function Approximation in MDPs
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Nonlinear Systems: Analysis, Stability, and Control
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Design Principles of the Hippocampal Cognitive Map
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A Laplacian Framework for Option Discovery in Reinforcement Learning
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The Laplacian in RL: Learning Representations with Efficient Approximations
On the Generalization of Representations in Reinforcement Learning
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No More Pesky Hyperparameters: Offline Hyperparameter Tuning for RL
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Proto-Value Networks: Scaling Representation Learning with Auxiliary Tasks
Jesse Farebrother, Joshua Greaves, Rishabh Agarwal, Charline Le Lan, Ross Goroshin, Pablo Samuel Castro, and Marc G. Bellemare · 2023
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Deep Laplacian-based Options for Temporally-Extended Exploration
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Temporal Abstraction in Reinforcement Learning with the Successor Representation
Marlos C. Machado, Andre Barreto, Doina Precup, and Michael Bowling · 2023
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