In this work we identify the dormant neuron phenomenon in deep reinforcement learning,
where an agent's network suffers from an increasing number of inactive neurons, thereby …

The recent rapid progress in (self) supervised learning models is in large part predicted by
empirical scaling laws: a model's performance scales proportionally to its size. Analogous …

Tomorrow's robots will need to distinguish useful information from noise when performing
different tasks. A household robot for instance may continuously receive a plethora of …

Abstract Dynamic Sparse Training (DST) is a rapidly evolving area of research that seeks to
optimize the sparse initialization of a neural network by adapting its topology during training …

Lifelong deep reinforcement learning (DRL) approaches are commonly employed to adapt
continuously to new tasks without forgetting previously acquired knowledge. While current …

Deep reinforcement learning (DRL) has been widely adopted in various applications, yet it
faces practical limitations due to high storage and computational demands. Dynamic sparse …

The loss of plasticity in learning agents, analogous to the solidification of neural pathways in
biological brains, significantly impedes learning and adaptation in reinforcement learning …

Deep Multi-agent Reinforcement Learning (MARL) relies on neural networks with numerous
parameters in multi-agent scenarios, often incurring substantial computational overhead …

In recent years, Dynamic Sparse Training (DST) has emerged as an alternative to post-
training pruning for generating efficient models. In principle, DST allows for a more memory …

Deep reinforcement learning has achieved remarkable performance in various domains by
leveraging deep neural networks for approximating value functions and policies. However …