# Reinforcement Learning-Based Energy-Aware Coverage Path Planning for Precision Agriculture

arXiv: https://arxiv.org/abs/2601.16405 · HTML: https://mlsanity.com/paper/2601.16405/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Placed in RL for energy, networks and operations, one of 15 directions in Deep reinforcement learning methods.
- Builds on 8 of its 36 references that are in the map.
- First posted on arXiv 23 Jan 2026.

## 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, 8 of 8)

- [Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor](https://mlsanity.com/paper/1801.01290/) (2018)
- [Addressing Function Approximation Error in Actor-Critic Methods](https://mlsanity.com/paper/1802.09477/) (2018)
- [Prioritized Information Bottleneck Theoretic Framework with Distributed Online Learning for Edge Video Analytics](https://mlsanity.com/paper/2409.00146/) (2024)
- [Enhancing Vehicular Platooning with Wireless Federated Learning: A Resource-Aware Control Framework](https://mlsanity.com/paper/2507.00856/) (2025)
- [Shared Spatial Memory Through Predictive Coding](https://mlsanity.com/paper/2511.04235/) (2025)
- [Fields2Cover: An open-source coverage path planning library for unmanned agricultural vehicles](https://mlsanity.com/paper/2210.07838/) (2022)
- [Energy-aware Multi-UAV Coverage Mission Planning with Optimal Speed of Flight](https://mlsanity.com/paper/2402.10529/) (2024)
- [Task-Oriented Semantic Compression for Localization at the Network Edge](https://mlsanity.com/paper/2504.18317/) (2025)

## Built on by (most cited first, 6 of 6)

- [Forget to Improve: On-Device LLM-Agent Continual Learning via Budget-Curated Memory](https://mlsanity.com/paper/2606.25115/) (2026)
- [Lifecycle-Aware Federated Continual Learning in Mobile Autonomous Systems](https://mlsanity.com/paper/2604.20745/) (2026)
- [CrystalMem: Elastic Memory for Self-Evolving LLM Agents via Knowledge Crystallization](https://mlsanity.com/paper/2608.00303/) (2026)
- [ERRAND: Budgeted Maintenance of Agent Memory](https://mlsanity.com/paper/2609.29545/) (2026)
- [When Unlearning Fails: Reliable Data Deletion under Post-Training in Agent Networks](https://mlsanity.com/paper/2607.28829/) (2026)
- [Not All Experience Belongs in the Weights: Component Routing for Self-Improving GUI Agents](https://mlsanity.com/paper/2610.01787/) (2026)

## Abstract (arXiv)

Coverage Path Planning (CPP) is a fundamental capability for agricultural robots; however, existing solutions often overlook energy constraints, resulting in incomplete operations in large-scale or resource-limited environments. This paper proposes an energy-aware CPP framework grounded in Soft Actor-Critic (SAC) reinforcement learning, designed for grid-based environments with obstacles and charging stations. To enable robust and adaptive decision-making under energy limitations, the framework integrates Convolutional Neural Networks (CNNs) for spatial feature extraction and Long Short-Term Memory (LSTM) networks for temporal dynamics. A dedicated reward function is designed to jointly optimize coverage efficiency, energy consumption, and return-to-base constraints. Experimental results demonstrate that the proposed approach consistently achieves over 90% coverage while ensuring energy safety, outperforming traditional heuristic algorithms such as Rapidly-exploring Random Tree (RRT), Particle Swarm Optimization (PSO), and Ant Colony Optimization (ACO) baselines by 13.4-19.5% in coverage and reducing constraint violations by 59.9-88.3%. These findings validate the proposed SAC-based framework as an effective and scalable solution for energy-constrained CPP in agricultural robotics.