MaDi: Learning to Mask Distractions for Generalization in Visual Deep Reinforcement Learning

1 code implementation • 23 Dec 2023 • Bram Grooten, Tristan Tomilin, Gautham Vasan, Matthew E. Taylor, A. Rupam Mahmood, Meng Fang, Mykola Pechenizkiy, Decebal Constantin Mocanu

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.

Data Augmentation

Fantastic Weights and How to Find Them: Where to Prune in Dynamic Sparse Training

1 code implementation • NeurIPS 2023 • Aleksandra I. Nowak, Bram Grooten, Decebal Constantin Mocanu, Jacek Tabor

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

1 code implementation • 13 Feb 2023 • Bram Grooten, Ghada Sokar, Shibhansh Dohare, Elena Mocanu, Matthew E. Taylor, Mykola Pechenizkiy, Decebal Constantin Mocanu

Tomorrow's robots will need to distinguish useful information from noise when performing different tasks.

reinforcement-learning Reinforcement Learning (RL) +1

Is Vanilla Policy Gradient Overlooked? Analyzing Deep Reinforcement Learning for Hanabi

1 code implementation • 22 Mar 2022 • Bram Grooten, Jelle Wemmenhove, Maurice Poot, Jim Portegies

In pursuit of enhanced multi-agent collaboration, we analyze several on-policy deep reinforcement learning algorithms in the recently published Hanabi benchmark.

reinforcement-learning Reinforcement Learning (RL)