A Cognitive Architecture Using Reinforcement Learning to Enable Autonomous Spacecraft Operations

We propose an architecture to enable the modular development and deployment of autonomous intelligent agents in support of spacecraft operations. This architecture supports both training and application of artificial intelligence models. It particularly enables the use of deep reinforcement learning for each module independently and jointly. Deep reinforcement learning is a technique that enables the automated learning of plans of action and has recently successfully been used, for example, to learn strategies for games like Go. Our proposed architecture provides a "utility" layer for generalized learning and a provides for independent functional modules that can be added, modified, or removed easily. It also accounts for intensive multicore computational needs. Lastly, it allows for desired behavior to be learned independently or in the context of the broader system. In Phase I, we will deliver a preliminary cognitive architecture, a feasibility study, a prototype of an autonomous agent, and a detailed plan to develop a comprehensive cognitive architecture feasibility study.