Plasticity, the ability of a neural network to quickly change its predictions in response to new
information, is essential for the adaptability and robustness of deep reinforcement learning …

A growing body of evidence suggests that neural networks employed in deep reinforcement
learning (RL) gradually lose their plasticity, the ability to learn from new data; however, the …

Underpinning the past decades of work on the design, initialization, and optimization of
neural networks is a seemingly innocuous assumption: that the network is trained on a\textit …

The success of deep learning in the computer vision and natural language processing
communities can be attributed to training of very deep neural networks with millions or …

The reinforcement learning (RL) problem is rife with sources of non-stationarity, making it a
notoriously difficult problem domain for the application of neural networks. We identify a …

Recent years have witnessed significant progresses in deep Reinforcement Learning (RL).
Empowered with large scale neural networks, carefully designed architectures, novel …

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 …

While improvements in deep learning architectures have played a crucial role in improving
the state of supervised and unsupervised learning in computer vision and natural language …

Deep reinforcement learning (RL) algorithms are predominantly evaluated by comparing
their relative performance on a large suite of tasks. Most published results on deep RL …

Abstract In Reinforcement Learning (RL), enhancing sample efficiency is crucial, particularly
in scenarios when data acquisition is costly and risky. In principle, off-policy RL algorithms …