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Found 5 papers, 3 papers with code
Demonstrate Once, Imitate Immediately (DOME): Learning Visual Servoing for One-Shot Imitation Learning
We present DOME, a novel method for one-shot imitation learning, where a task can be learned from just a single demonstration and then be deployed immediately, without any further data collection or training.
End-To-End Semi-supervised Learning for Differentiable Particle Filters
Recent advances in incorporating neural networks into particle filters provide the desired flexibility to apply particle filters in large-scale real-world applications.
Imitation Learning with Sinkhorn Distances
In this paper, we present tractable solutions by formulating imitation learning as minimization of the Sinkhorn distance between occupancy measures.
Deep Reinforcement Learning for Control of Probabilistic Boolean Networks
Probabilistic Boolean Networks (PBNs) were introduced as a computational model for the study of complex dynamical systems, such as Gene Regulatory Networks (GRNs).
A Bayesian Ensemble Regression Framework on the Angry Birds Game
This has the advantage to establish an informative feature space and modify the task of game playing to a regression analysis problem.
