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Found 6 papers, 5 papers with code
Nerva: a Truly Sparse Implementation of Neural Networks
We introduce Nerva, a fast neural network library under development in C++.
Boosting Robustness in Preference-Based Reinforcement Learning with Dynamic Sparsity
It becomes essential that agents learn to focus on the subset of task-relevant environment features.
MaDi: Learning to Mask Distractions for Generalization in Visual Deep Reinforcement Learning
Our algorithm improves the agent's focus with useful masks, while its efficient Masker network only adds 0. 2% more parameters to the original structure, in contrast to previous work.
Fantastic Weights and How to Find Them: Where to Prune in Dynamic Sparse Training
The key components of this framework are the pruning and growing criteria, which are repeatedly applied during the training process to adjust the network's sparse connectivity.
Automatic Noise Filtering with Dynamic Sparse Training in Deep Reinforcement Learning
Tomorrow's robots will need to distinguish useful information from noise when performing different tasks.
Is Vanilla Policy Gradient Overlooked? Analyzing Deep Reinforcement Learning for Hanabi
In pursuit of enhanced multi-agent collaboration, we analyze several on-policy deep reinforcement learning algorithms in the recently published Hanabi benchmark.
