Reinforcement Learning Scheduler for Solar-Powered Edge Devices

Edge devices powered by renewable energy sources, such as solar panels, face the challenge of operating under uncertain and intermittent energy availability. This paper proposes a Reinforcement Learning (RL) scheduler for heavy tasks, e.g. image classification running on solar-powered devices with an accumulator.The scheduler maximizes the total number of processed images by deciding whether to process an image immediately or store it for later processing, all while remaining energy-aware to prevent device shutdown. Crucially, the method operates without any prior knowledge of future solar energy production, making it suitable for devices lacking an internet connection for forecasting. The RL method processes 99.8% of the images processed by the optimal solution obtained through Integer Linear Programming (ILP), missing on average only 224 seconds of captured frames per day. This demonstrates that RL is a viable alternative to optimal solvers even in forecast-free scenarios