This paper reviews exploration techniques in deep reinforcement learning. Exploration
techniques are of primary importance when solving sparse reward problems. In sparse …

Traditionally, cognitive and computer scientists have viewed intelligence solipsistically, as a
property of unitary agents devoid of social context. Given the success of contemporary …

Reinforcement learning algorithms typically struggle in the absence of a dense, well-shaped
reward function. Intrinsically motivated exploration methods address this limitation by …

While deep reinforcement learning (RL) has fueled multiple high-profile successes in
machine learning, it is held back from more widespread adoption by its often poor data …

The original" Seven Motifs" set forth a roadmap of essential methods for the field of scientific
computing, where a motif is an algorithmic method that captures a pattern of computation …

Abstract The study of Variational Quantum Eigensolvers (VQEs) has been in the spotlight in
recent times as they may lead to real-world applications of near-term quantum devices …

In reinforcement learning (RL), balancing exploration and exploitation is crucial for
achieving an optimal policy in a sample-efficient way. To this end, existing sample-efficient …

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

In this article, a novel reinforcement learning (RL) method is developed to solve the optimal
tracking control problem of unknown nonlinear multiagent systems (MASs). Different from …

Deep reinforcement learning (DRL) and deep multiagent reinforcement learning (MARL)
have achieved significant success across a wide range of domains, including game artificial …