Applying QMIX to Active Wake Control: Multi-Agent Reinforcement Learning

When multiple wind turbines are positioned close to one another, such as in a wind farm, wind turbines located downwind of other turbines are not 100% efficient due to wakes, negatively affecting the total power output of the wind farm. A way to mitigate the loss of power is to steer the wake away from the next turbine, which lowers the current...

Learning scalable and efficient communication policies for multi-robot collision avoidance

Decentralized multi-robot systems typically perform coordinated motion planning by constantly broadcasting their intentions to avoid collisions. However, the risk of collision between robots varies as they move and communication may not always be needed. This paper presents an efficient communication method that addresses the problem of “when...

The Effects of Entropy Regularization and Lyapunov Stability Constraint on Multi-Agent Reinforcement Learning for Autonomous Driving

High level decision making in Autonomous Driving (AD) is a challenging task due to the presence of multiple actors and complex driving interactions. Multi-Agent Reinforcement Learning (MARL) has been proposed to learn multiple driving policies concurrently to solve AD tasks. In the literature, multi-agent algorithms have been shown to outperform...

Difference rewards policy gradients

Policy gradient methods have become one of the most popular classes of algorithms for multi-agent reinforcement learning. A key challenge, however, that is not addressed by many of these methods is multi-agent credit assignment: assessing an agent’s contribution to the overall performance, which is crucial for learning good policies. We...

Bus management using multi-agent reinforcement learning

Bus bunching is a problem that occurs in many high frequent bus systems. This can be averted by several countermeasures of which holding control is the most popular one in practice. Holding control strategies are often implemented using predefined rules. In this study, multi-agent reinforcement learning is selected to develop an effective...

Human Aspects in Collaborative Order Picking – What if Robots Learned How to Give Humans a Break?

Human aspects in collaboration of humans and robots, as common in warehousing, are considered increasingly important objectives in operations management. In this work, we let robots learn about human stress levels based on sensor data in collaborative order picking of robotic mobile fulfillment systems. To this end, we develop a multi-agent...