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Found 7 papers, 1 papers with code
More Efficient Exploration with Symbolic Priors on Action Sequence Equivalences
Incorporating prior knowledge in reinforcement learning algorithms is mainly an open question.
Better state exploration using action sequence equivalence
Incorporating prior knowledge in reinforcement learning algorithms is mainly an open question.
There Is No Turning Back: A Self-Supervised Approach for Reversibility-Aware Reinforcement Learning
We propose to learn to distinguish reversible from irreversible actions for better informed decision-making in Reinforcement Learning (RL).
Interferometric Graph Transform for Community Labeling
We present a new approach for learning unsupervised node representations in community graphs.
Low-Rank Projections of GCNs Laplacian
In this work, we study the behavior of standard models for community detection under spectral manipulations.
A spectral perspective on GCNs
In this work, we study the behavior of standard GCNs under spectral manipulations.
Geometric Deep Reinforcement Learning for Dynamic DAG Scheduling
In this paper, we propose a reinforcement learning approach to solve a realistic scheduling problem, and apply it to an algorithm commonly executed in the high performance computing community, the Cholesky factorization.
