Energy systems undergo major transitions to facilitate the large-scale penetration of
renewable energy technologies and improve efficiencies, leading to the integration of many …

Reinforcement learning (RL) interacts with the environment to solve sequential decision-
making problems via a trial-and-error approach. Errors are always undesirable in real-world …

The advances in reinforcement learning have recorded sublime success in various domains.
Although the multi-agent domain has been overshadowed by its single-agent counterpart …

Deep reinforcement learning (RL) has achieved outstanding results in recent years. This has
led to a dramatic increase in the number of applications and methods. Recent works have …

Multi-agent deep reinforcement learning (MARL) suffers from a lack of commonly-used
evaluation tasks and criteria, making comparisons between approaches difficult. In this work …

Communication is an effective mechanism for coordinating the behavior of multiple agents.
In the field of multi-agent reinforcement learning, agents can improve the overall learning …

Sharing parameters in multi-agent deep reinforcement learning has played an essential role
in allowing algorithms to scale to a large number of agents. Parameter sharing between …

Exploration in multi-agent reinforcement learning is a challenging problem, especially in
environments with sparse rewards. We propose a general method for efficient exploration by …

This paper surveys the field of deep multiagent reinforcement learning (RL). The
combination of deep neural networks with RL has gained increased traction in recent years …

Device-to-device (D2D) technology, which allows direct communications between proximal
devices, is widely acknowledged as a promising candidate to alleviate the mobile traffic …