This research seeks to quantify the impact of the choice of reward function on behavioral
diversity in learning robot teams. The methodology developed for this work has been …

In most research involving the use of reinforcement learning techniques for training robots,
task and reward are tightly coupled: the task for the learning agent is to maximize its reward …

This paper describes research investigating behavioral specialization in learning robot
teams. Each agent is provided a common set of skills (motor schema-based behavioral …

This research investigates behavioral diversity in learning robot teams. Behavioral diversity
refers to the extent to which individual agents assume distinct behavioral roles in a group …

Quantitative relationships between performance and behavioral diversity are investigatedin
a multi-robotforaging task. The task, referred to as m&i-foraging, requires robots to collect …

A key challenge in designing robot teams is determining how to allocate team members to
specific roles according to their abilities and the demands of the environment. In this paper …

This thesis addresses the problem of setting the balance between exploration and
exploitation in teams of learning robots who exchange information. Specifically it looks at …

In the research area of multi-robot systems, several researchers have reported on consistent
success in using heuristic measures to improve loose coordination in teams, by minimizing …

In the research area of multi-robot systems, several researchers have reported on consistent
success in using heuristic measures to improve loose coordination in teams, by minimizing …

The multiagent reinforcement learning approach is now widely applied to cause agents to
behave rationally in a multiagent system. However, due to the complex interactions in a …