Multiagent learning offers a rich framework to address challenging real-world problems such as remote exploration and healthcare coordination, which require autonomous agents to express elaborate interactions. To be effective in such systems, agents must collectively reason about and pursue high-level, long-term, and possibly nebulous objectives while adapting their strategy to...
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...
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...
Autonomous agents that sense, decide, act, and coordinate effectively with each other are critical in many real-world domains such as autonomous driving, search and rescue missions, air traffic management, and underwater or deep space exploration. All such domains share a key difficulty: though high-level mission goals are clear to system...
It is desirable for complex engineered systems to perform missions efficiently and economically, even when these missions' complex, variable, long-term operational profiles make it likely for hazards to arise. It is thus important to design these systems to be resilient so that they will actively prevent and recover from hazards...
Performing autonomous robotic tasks in the field, such as ocean monitoring and aerial surveillance, requires planning and executing paths in dynamic environments. In these uncertain and changing environments, it is not uncommon to see a large difference between the path planned by the robotic vehicle and the path that the...
Cephalopod-inspired soft robot arms have been studied as a new type of manipulator. These arms have been proposed for reach and grab tasks, underwater locomotion via swimming and walking, and robotic surgery. However, the capabilities of existing soft arms are limited to simple motions and light loads. Arm design has...
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...
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...
Complex engineered systems design is a collaborative activity. To design a system, experts from the relevant disciplines must work together to create the best overall system from their individual components. This situation is analogous to a multiagent system in which agents solve individual parts of a larger problem. Current multiagent...