The use of autonomous robots in complex exploration tasks is rapidly increasing. Indeed, robots can provide speed and cost effectiveness in many tasks, as well as allow operation in environments that are hostile to humans. In this dissertation we: 1) provide two adaptive navigation algorithms; 2) develop a coordination mechanism;...
Coordination in large multiagent systems in order to achieve a system level goal is a critical challenge. Given the agents' intention to cooperate, there is no guarantee that the agent actions will lead to good system objective especially when the system becomes large. One of the primary difficulties in such...
Multi-robot teams offer promising solutions for many long term deployments in remote and dangerous domains, such as extraterrestrial or underseas exploration. However, long term deployments present many problems preventing robot teams from operating effectively. Learning over long time scales is makes it difficult to assign credit to robots' actions, as...
Coordinating multiple robots to achieve a complex task requires solving two distinct control problems: the high-level control problem of ensuring that each robot aims to perform a useful task (e.g., coordination) and the low-level control problem of ensuring that each robot actually performs the correct actions to achieve its task...
Recent advances in multiagent learning have led to exciting new capabilities spanning fields as diverse as planetary exploration, air traffic control, military reconnaissance, and airport security. Such algorithms provide a tangible benefit over traditional control algorithms in that they allow fast responses, adapt to dynamic environments, and generally scale well....
Reinforcement learning has made impressive strides in solving problems in challenging domains, but problems are increasingly being described with sparse rewards. Sparse rewards directly reduce the rate at which useful feedback is provided to the learner and make it difficult to distinguish between what specific actions led to the reception...
Multiagent approaches are well suited to designing autonomous solutions for systems that feature complex interactions between many individuals such as in autonomous traffic systems and multi-robot exploration systems. However, creating autonomous agents that function effectively in these systems is a challenging task. In these complex environments, agents need informative reward...
In this thesis, we introduce alignment-based algorithms for improving the performance of reinforcement learning solutions for problems where the reward signal cannot be collapsed into a single number. Many real world problems require an agent to balance performance, longevity, and safety, and do so across different timelines. The key to...
Coordination is essential to achieving good performance in cooperative multiagent systems. To date, most work has focused on either implicit or explicit coordination mechanisms, while relatively little work has focused on the benefits of combining these two approaches. In this work we demonstrate that combining explicit and implicit mechanisms can...
There is growing commercial interest in the use of multiagent systems in real world applications. Some examples include inventory management in warehouses, smart homes, planetary exploration, search and rescue, air-traffic management and autonomous transportation systems. However, multiagent coordination is an extremely challenging problem. First, information relevant for coordination is often...