We introduce a value-based RL agent, which we call BBF, that achieves super-human
performance in the Atari 100K benchmark. BBF relies on scaling the neural networks used …

Artificial neural networks, deep-learning methods and the backpropagation algorithm form
the foundation of modern machine learning and artificial intelligence. These methods are …

In this paper, we characterize the behavior of canonical value-based deep reinforcement
learning (RL) approaches under varying degrees of non-stationarity. In particular, we …

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 …

Modern deep-learning systems are specialized to problem settings in which training occurs
once and then never again, as opposed to continual-learning settings in which training …

Value functions are a central component of deep reinforcement learning (RL). These
functions, parameterized by neural networks, are trained using a mean squared error …

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 …

Recent advancements in off-policy Reinforcement Learning (RL) have significantly improved
sample efficiency, primarily due to the incorporation of various forms of regularization that …

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 …