Abstract Deep Reinforcement Learning has made significant progress in multi-agent
systems in recent years. The aim of this review article is to provide an overview of recent …

Deep learning is transforming most areas of science and technology, including electron
microscopy. This review paper offers a practical perspective aimed at developers with …

This work identifies a common flaw of deep reinforcement learning (RL) algorithms: a
tendency to rely on early interactions and ignore useful evidence encountered later …

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 …

We present DrQ-v2, a model-free reinforcement learning (RL) algorithm for visual
continuous control. DrQ-v2 builds on DrQ, an off-policy actor-critic approach that uses data …

The disassembly complexity of end-of-life products increases continuously. Traditional
methods are facing difficulties in solving the decision-making and control problems of …

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 …

A key theme in the past decade has been that when large neural networks and large
datasets combine they can produce remarkable results. In deep reinforcement learning (RL) …

Games are abstractions of the real world, where artificial agents learn to compete and
cooperate with other agents. While significant achievements have been made in various …

Generative flow networks (GFlowNets) are a family of algorithms that learn a generative
policy to sample discrete objects $ x $ with non-negative reward $ R (x) $. Learning …