You will be a summer student intern in the Telecommunications Architectures Group (332C) supporting the design, development and implementation of Artificial Intelligence (AI) techniques and algorithms to manage Delay Tolerant Networks (DTNs). Together with other engineers in our section, you will investigate the use of reinforcement learning to control a DTN node’s configuration parameters at different levels of the protocol stack (Bundle Protocol and Licklider Transmission Protocol). Initially, this study will survey different reinforcement learning techniques to identify which are best suited for the problem at hand. Then, interfaces to monitor/control the Interplanetary Overlay Network (ION) will be developed and integrated with the down-selected set of AI technologies. Complexity of the control actions to be performed by the AI system will be incrementally build as a function of the available time and resources.