In this review, we present an analysis of the most used multi-agent reinforcement learning
algorithms. Starting with the single-agent reinforcement learning algorithms, we focus on the …

Deep reinforcement learning has produced many success stories in recent years. Some
example fields in which these successes have taken place include mathematics, games …

Recent years have witnessed significant advances in reinforcement learning (RL), which
has registered tremendous success in solving various sequential decision-making problems …

Agriculture is a vital sector in develo** nations such as India, and the use of autonomous
vehicles and Internet of Things (IoT) technology has the potential to revolutionize farming …

Deep Reinforcement Learning (DRL) has recently witnessed significant advances that have
led to multiple successes in solving sequential decision-making problems in various …

Reinforcement learning combined with neural networks has recently led to a wide range of
successes in learning policies in different domains. For robot manipulation, reinforcement …

Efficient exploration of unknown environments is a fundamental precondition for modern
autonomous mobile robot applications. Aiming to design robust and effective robotic …

Unmanned aerial vehicles (UAVs) are considered as one of the promising technologies for
the next-generation wireless communication networks. Their mobility and their ability to …

Multi-agent reinforcement learning (MARL) has been a rapidly evolving field. This paper
presents a comprehensive survey of MARL and its applications. We trace the historical …

Multi-agent reinforcement learning (MARL) algorithms often suffer from an exponential
sample complexity dependence on the number of agents, a phenomenon known as the …