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Reinforcement Learning for Efficient Edge Computing Solutions
Edge computing is a recent technique, which is driving the cloud support industry. Enterprises are looking for options to compute data at the edge to make an efficient and quick service platform. Robotics is at the center of edge computing. Autonomous edge robots are at the heart of the vision of almost all the enterprises.
Robots are smart objects that have AI embedded in its workbench. Amazon has recently launched DeepRacer, which is a highly functional AI-based autonomous vehicle. Devices like DeepLens and Echo represent a significant shift in AI development for the edge. Enterprises are looking to develop more AI-driven applications including robotics on their workbenches. IoT devices have also become a prominent workbench for advanced AI applications that can operate autonomously.
Reinforcement learning is a methodology, algorithms, and workflows that are applied to robotics and other development initiatives where AI-driven processes are used. It is also used in many deep learning initiatives. AI service providers are shifting towards RL-oriented workbenches that execute almost all DevOps pipeline functions. Let’s delve deeper into the applications of Reinforcement learning:
Edge-AI Modeling and Training: RL technology allows its users to build, train, and deploy robotics and other AI-based application through several built-in RL frameworks like Intel coach, Ray RL.
Edge-AI simulation: Reinforcement learning extends the open-source Robot Operating System with connectivity to cloud computing solutions like machine learning, monitoring, and analytics. RL also enables the robots to stream, communicate, navigate, and learn data.
AI DevOps: RL technology allows AI robotics developers to start application development with a single click in the cloud support console. It provides a managed service that collects data from distributed services and generates real-time key performance indicators and matrices that allows the enterprises to make informed and better decisions at the edge.