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4 papers · 2 filters
PODNet: A Neural Network for Discovery of Plannable Options
Ritwik Bera, Vinicius G. Goecks, Gregory M. Gremillion +2
Learning from demonstration has been widely studied in machine learning but becomes challenging when the demonstrated trajectories are unstructured and follow different objectives.…
Integrating Behavior Cloning and Reinforcement Learning for Improved Performance in Dense and Sparse Reward Environments
Vinicius G. Goecks, Gregory M. Gremillion, Vernon J. Lawhern +2
This paper investigates how to efficiently transition and update policies, trained initially with demonstrations, using off-policy actor-critic reinforcement learning. It is well-k…
HONEM: Learning Embedding for Higher Order Networks
Mandana Saebi, Giovanni Luca Ciampaglia, Lance M Kaplan +1
Representation learning on networks offers a powerful alternative to the oft painstaking process of manual feature engineering, and as a result, has enjoyed considerable success in…
GLEE: Geometric Laplacian Eigenmap Embedding
Leo Torres, Kevin S Chan, Tina Eliassi-Rad
Graph embedding seeks to build a low-dimensional representation of a graph G. This low-dimensional representation is then used for various downstream tasks. One popular approach is…