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Found 9 papers, 4 papers with code
Effect of Optimizer, Initializer, and Architecture of Hypernetworks on Continual Learning from Demonstration
In this paper, we perform an exploratory study of the effects of different optimizers, initializers, and network architectures on the continual learning performance of hypernetworks for CLfD.
GRINN: A Physics-Informed Neural Network for solving hydrodynamic systems in the presence of self-gravity
This is in contrast to the scaling of the grid-based code for the hydrodynamic and self-gravity calculations as the number of dimensions is increased.
Action Noise in Off-Policy Deep Reinforcement Learning: Impact on Exploration and Performance
Many Deep Reinforcement Learning (D-RL) algorithms rely on simple forms of exploration such as the additive action noise often used in continuous control domains.
Using Bayesian Deep Learning to infer Planet Mass from Gaps in Protoplanetary Disks
A unique feature of our approach is that it can distinguish between the uncertainty associated with the deep learning architecture and uncertainty inherent in the input data due to measurement noise.
Continual Learning from Demonstration of Robotics Skills
We empirically demonstrate the effectiveness of this approach in remembering long sequences of trajectory learning tasks without the need to store any data from past demonstrations.
DPNNet-2.0 Part I: Finding hidden planets from simulated images of protoplanetary disk gaps
The observed sub-structures, like annular gaps, in dust emissions from protoplanetary disk, are often interpreted as signatures of embedded planets.
How do Offline Measures for Exploration in Reinforcement Learning behave?
Sufficient exploration is paramount for the success of a reinforcement learning agent.
A Machine Learning model to infer planet masses from gaps observed in protoplanetary disks
To this end, we introduce DPNNet (Disk Planet Neural Network), an efficient model of planetary gaps by exploiting the power of machine learning.
Earth and Planetary Astrophysics
Hierarchical Control for Bipedal Locomotion using Central Pattern Generators and Neural Networks
Artificial central pattern generators (CPGs) can produce synchronized joint movements and have been used in the past for bipedal locomotion.
