Reinforcement learning is the area of machine learning concerned with learning which
actions to execute in an unknown environment in order to maximize cumulative reward. As …

The RoboCup 2D simulation domain has served as a platform for research in AI, machine
learning, and multiagent systems for more than two decades. However, for the researcher …

This paper investigates the use of model-based reinforcement learning in the context of ad
hoc teamwork. We introduce a novel approach, named TEAMSTER, where we propose …

Roles such as leading and following can emerge naturally in human groups. However, in
human–robot teams, such roles are often predefined due to the difficulty of scalably learning …

Abstract The Standard Platform League is one of the main competitions at the annual
RoboCup world championships. In this competition, teams of five humanoid robots play …

Abstract We present the Bayesian Online Prediction for Ad hoc teamwork (BOPA), a novel
algorithm for ad hoc teamwork which enables a robot to collaborate, on the fly, with human …

Ad hoc teamwork is a research topic in multi-agent systems whereby an agent (the “ad hoc
agent”) must successfully collaborate with a set of unknown agents (the “teammates”) …

Agents can learn to improve their coordination with their teammates and increase team
performance. There are finite training instances, where each training instance is an …

As robots become more affordable, they will begin to exist in the world in greater quantities.
Some of these robots will likely be designed to act as components in specific teams. These …

This thesis is concerned with the ad hoc coordination problem. Therein, the goal is to design
an autonomous agent which can achieve high flexibility and efficiency in a multiagent …