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

Intelligent agents stand out as a potential path toward artificial general intelligence (AGI).
Thus, researchers have dedicated significant effort to diverse implementations for them …

Abstract The field of Multi-Agent Reinforcement Learning (MARL) is currently facing a
reproducibility crisis. While solutions for standardized reporting have been proposed to …

The necessity for cooperation among intelligent machines has popularised cooperative multi-
agent reinforcement learning (MARL) in AI research. However, many research endeavours …

Benchmarks play an important role in the development of machine learning algorithms. For
example, research in reinforcement learning (RL) has been heavily influenced by available …

Population-based multi-agent reinforcement learning (PB-MARL) encompasses a range of
methods that merge dynamic population selection with multi-agent reinforcement learning …

Integrating sparse code multiple access (SCMA) and mobile edge computing (MEC) into the
Internet of Things (IoT) networks can enable efficient connectivity and timely computation for …

Reinforcement learning (RL) is a versatile framework for optimizing long-term goals.
Although many real-world problems can be formalized with RL, learning and deploying a …

Multi-agent Reinforcement Learning (MARL) has gained wide attention in recent years and
has made progress in various fields. Specifically, cooperative MARL focuses on training a …

Obstacle avoidance in UAV swarms is crucial for ensuring the stability and safety of cluster
flights. However, traditional methods of swarm obstacle avoidance often fail to meet the …