Multiagent Reinforcement Learning (RL) solves complex tasks that require coordination with
other agents through autonomous exploration of the environment. However, learning a …

Cloud Computing provides an effective platform for executing large-scale and complex
workflow applications with a pay-as-you-go model. Nevertheless, various challenges …

The key challenge in multiagent learning is learning a best response to the behaviour of
other agents, which may be non-stationary: if the other agents adapt their strategy as well …

Reinforcement Learning has long been employed to solve sequential decision-making
problems with minimal input data. However, the classical approach requires a large number …

While recent work in reinforcement learning (RL) has led to agents capable of solving
increasingly complex tasks, the issue of high sample complexity is still a major concern. This …

Reinforcement Learning (RL) is an effective method for adaptive traffic signals control. As
one type of RL, the teacher-student framework has been found helpful in improving the …

Action advising is a popular and effective approach to accelerating independent multiagent
reinforcement learning (MARL), especially for the learning systems that all the agents learn …

Providing Reinforcement Learning agents with expert advice can dramatically improve
various aspects of learning. Prior work has developed teaching protocols that enable agents …

The exploration mechanism used by a Deep Reinforcement Learning (RL) agent plays a key
role in determining its sample efficiency. Thus, improving over random exploration is crucial …

Abstract Unmanned Aerial Vehicle (UAV) maneuver strategy learning remains a challenge
when using Reinforcement Learning (RL) in this sparse reward task. In this paper, we …