METHODS AND SYSTEMS FOR IMPLEMENTING DEEP REINFORCEMENT MODULE NETWORKS FOR AUTONOMOUS SYSTEMS CONTROL

A novel architecture for a network of deep reinforcement modules that enables cross-functional and multi-system coordination of autonomous systems for self-optimization with a reduced computational footprint is disclosed. Each deep reinforcement module in the network is comprised of either a single artificial neural network or a deep reinforcement module sub-network. DReMs are designed independently, decoupling each requisite function. Each module of a deep reinforcement module network is trained independently through deep reinforcement learning. By separating the functions into deep reinforcement modules, reward functions can be designed for each individual function, further simplifying the development of a full suite of algorithms while also minimizing training time. Following training, the DReMs are integrated into the full deep reinforcement module network, which is then refined through additional reinforcement training or genetic multi-objective optimization to maximize the overall performance of the network.