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Filtering by: Creator Sukhatme, Gaurav S. Remove constraint Creator: Sukhatme, Gaurav S. Advisor Tumer, Kagan Remove constraint Advisor: Tumer, Kagan Resource Type Dissertation Remove constraint Resource Type: Dissertation

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  • Learning to coordinate in sparse asymmetric multiagent systems

    Creator:
    Dixit, Gaurav
    Abstract:
    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...
    Resource Type:
    Dissertation
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    AN ABSTRACT OF THE DISSERTATION OF Gaurav Dixit for the

  • Navigation and coordination of autonomous mobile robots with limited resources

    Creator:
    Knudson, Matthew D.
    Abstract:
    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;...
    Resource Type:
    Dissertation
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    average focused objective r(s) per episode is calculated for all algorithms, though the neuro-evolutionary

  • Robust Long Term Autonomy Through Local Behaviors

    Creator:
    Yates, Connor L.
    Abstract:
    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...
    Resource Type:
    Dissertation
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    - tegrated into Agent 0’s population at epoch 20, Agent 0 overcomes the environmental difficulty causing both

  • Providing Informative Feedback for Learning in Tightly Coupled Multiagent Domains

    Creator:
    Rockefeller, Golden C.
    Abstract:
    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...
    Resource Type:
    Dissertation
    Full Text:
    described as a tuple (S,A, P,R,Ω, O, γ). S in the set of possible states. A is the set of possible actions

  • Tackling Credit Assignment Using Memory and Multilevel Optimization for Multiagent Reinforcement Learning

    Creator:
    Khadka, Shauharda
    Abstract:
    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...
    Resource Type:
    Dissertation
    Full Text:
    noise (0’s) of variable length. At each intro- duction of a signal (1/-1), the network must determine

  • Counterfactual thinking applied to multiagent systems

    Creator:
    Zerbel, Nicholas
    Abstract:
    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...
    Resource Type:
    Dissertation

  • Learning based methods applied to the MAV control problem

    Creator:
    Salichon, Max
    Abstract:
    This thesis addresses Micro Aerial Vehicle (MAV) control by leveraging learning based techniques to improve robustness of the control system. Applying classical control methods to MAVs is a difficult process due to the complexity of the control laws with fast and highly non-linear dynamics. These methods are mostly based on...
    Resource Type:
    Dissertation
    Full Text:
    between 15 and 60 cm (6 to 24 inches). The flight speed of those MAVs is on average between 5 and 20 m/s