# Autonomous Management of Energy-Harvesting IoT Nodes Using Deep Reinforcement Learning

## TL;DR

This paper demonstrates that deep reinforcement learning, specifically policy-gradient methods, effectively manages energy-harvesting IoT nodes autonomously, outperforming previous approaches and reducing manual configuration.

## Contribution

The paper introduces the application of advanced policy-gradient RL methods to IoT energy management, enabling more autonomous and efficient operation with less manual tuning.

## Key findings

- Policy-gradient RL outperforms previous methods in IoT management.
- Continuous observation and action modeling improve problem-solving capabilities.
- Custom reward functions align better with application goals.

## Abstract

Reinforcement learning (RL) is capable of managing wireless, energy-harvesting IoT nodes by solving the problem of autonomous management in non-stationary, resource-constrained settings. We show that the state-of-the-art policy-gradient approaches to RL are appropriate for the IoT domain and that they outperform previous approaches. Due to the ability to model continuous observation and action spaces, as well as improved function approximation capability, the new approaches are able to solve harder problems, permitting reward functions that are better aligned with the actual application goals. We show such a reward function and use policy-gradient approaches to learn capable policies, leading to behavior more appropriate for IoT nodes with less manual design effort, increasing the level of autonomy in IoT.