In a standard view of the reinforcement learning problem, an agent's goal is to efficiently
identify a policy that maximizes long-term reward. However, this perspective is based on a …

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 …

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 …

Pretrained language models (PLMs) are today the primary model for natural language
processing. Despite their impressive downstream performance, it can be difficult to apply …

In continual learning, plasticity refers to the ability of an agent to quickly adapt to new
information. Neural networks are known to lose plasticity when processing non-stationary …

Visual reinforcement learning (RL) has shown promise in continuous control tasks. Despite
its progress, current algorithms are still unsatisfactory in virtually every aspect of 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 …

An agent that efficiently accumulates knowledge to develop increasingly sophisticated skills
over a long lifetime could advance the frontier of artificial intelligence capabilities. The …

In value-based deep reinforcement learning with replay memories, the batch size parameter
specifies how many transitions to sample for each gradient update. Although critical to the …

The advancement of continual reinforcement learning (RL) has been facing various
obstacles, including standardized metrics and evaluation protocols, demanding …