# Multi-Task Lifelong Reinforcement Learning for Wireless Sensor Networks

arXiv: https://arxiv.org/abs/2506.16254 · HTML: https://mlsanity.com/paper/2506.16254/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in RL for energy, networks and operations, one of 15 directions in Deep reinforcement learning methods.
- Builds on 1 of its 17 references that are in the map.
- First posted on arXiv 19 Jun 2025.

## Placed in

- Deep reinforcement learning methods / [RL for energy, networks and operations](https://mlsanity.com/deeprl/rl-for-energy-networks-and-operations/)

## Builds on (most cited first, 1 of 1)

- [UAV-Aided Lifelong Learning for AoI and Energy Optimization in Non-Stationary IoT Networks](https://mlsanity.com/paper/2312.00334/) (2023)

## Abstract (arXiv)

Enhancing the sustainability and efficiency of wireless sensor networks (WSN) in dynamic and unpredictable environments requires adaptive communication and energy harvesting strategies. We propose a novel adaptive control strategy for WSNs that optimizes data transmission and EH to minimize overall energy consumption while ensuring queue stability and energy storing constraints under dynamic environmental conditions. The notion of adaptability therein is achieved by transferring the known environment-specific knowledge to new conditions resorting to the lifelong reinforcement learning concepts. We evaluate our proposed method against two baseline frameworks: Lyapunov-based optimization, and policy-gradient reinforcement learning (RL). Simulation results demonstrate that our approach rapidly adapts to changing environmental conditions by leveraging transferable knowledge, achieving near-optimal performance approximately 30% faster than the RL method and 60% faster than the Lyapunov-based approach. The implementation is available at our GitHub repository for reproducibility purposes [1].