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Found 8 papers, 4 papers with code
RRLS : Robust Reinforcement Learning Suite
We introduce the Robust Reinforcement Learning Suite (RRLS), a benchmark suite based on Mujoco environments.
Time-Constrained Robust MDPs
Robust reinforcement learning is essential for deploying reinforcement learning algorithms in real-world scenarios where environmental uncertainty predominates.
Look where you look! Saliency-guided Q-networks for generalization in visual Reinforcement Learning
This implies that training of a policy with small generalization gap should focus on such important pixels and ignore the others.
Local Feature Swapping for Generalization in Reinforcement Learning
We also demonstrate the effectiveness of CLOP as a general regularization technique in supervised learning.
Disentanglement by Cyclic Reconstruction
This enables the isolation of task-specific information from both domains and a projection into a common representation.
Numerical influence of ReLU'(0) on backpropagation
In theory, the choice of ReLU(0) in [0, 1] for a neural network has a negligible influence both on backpropagation and training.
Numerical influence of ReLU’(0) on backpropagation
In theory, the choice of ReLU(0) in [0, 1] for a neural network has a negligible influence both on backpropagation and training.
Disentangled cyclic reconstruction for domain adaptation
The domain adaptation problem involves learning a unique classification or regres-sion model capable of performing on both a source and a target domain.
