# Dynamic Collaborative Multi-Agent Reinforcement Learning Communication for Autonomous Drone Reforestation

arXiv: https://arxiv.org/abs/2211.15414 · HTML: https://mlsanity.com/paper/2211.15414/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 30 of its 85 references that are in the map.
- First posted on arXiv 14 Nov 2022.

## Builds on (most cited first, 30 of 30)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [Neural Message Passing for Quantum Chemistry](https://mlsanity.com/paper/1704.01212/) (2017)
- [Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering](https://mlsanity.com/paper/1606.09375/) (2016)
- [Graph Neural Networks: A Review of Methods and Applications](https://mlsanity.com/paper/1812.08434/) (2018)
- [Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments](https://mlsanity.com/paper/1706.02275/) (2017)
- [DeepWalk: Online Learning of Social Representations](https://mlsanity.com/paper/1403.6652/) (2014)
- [Relational inductive biases, deep learning, and graph networks](https://mlsanity.com/paper/1806.01261/) (2018)
- [Dota 2 with Large Scale Deep Reinforcement Learning](https://mlsanity.com/paper/1912.06680/) (2019)
- [IMPALA: Scalable Distributed Deep-RL with Importance Weighted Actor-Learner Architectures](https://mlsanity.com/paper/1802.01561/) (2018)
- [Gated Graph Sequence Neural Networks](https://mlsanity.com/paper/1511.05493/) (2015)
- [Learning to Communicate with Deep Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/1605.06676/) (2016)
- [Safe, Multi-Agent, Reinforcement Learning for Autonomous Driving](https://mlsanity.com/paper/1610.03295/) (2016)
- [Thinking Fast and Slow with Deep Learning and Tree Search](https://mlsanity.com/paper/1705.08439/) (2017)
- [The NetHack Learning Environment](https://mlsanity.com/paper/2006.13760/) (2020)
- [Open-Ended Learning Leads to Generally Capable Agents](https://mlsanity.com/paper/2107.12808/) (2021)
- [Scalable Evaluation of Multi-Agent Reinforcement Learning with Melting Pot](https://mlsanity.com/paper/2107.06857/) (2021)
- [A Survey of Learning in Multiagent Environments: Dealing with Non-Stationarity](https://mlsanity.com/paper/1707.09183/) (2017)
- [Reinforcement Learning in Economics and Finance](https://mlsanity.com/paper/2003.10014/) (2020)
- [Learning Individually Inferred Communication for Multi-Agent Cooperation](https://mlsanity.com/paper/2006.06455/) (2020)
- [Efficient Communication in Multi-Agent Reinforcement Learning via Variance Based Control](https://mlsanity.com/paper/1909.02682/) (2019)
- [On the Importance of Environments in Human-Robot Coordination](https://mlsanity.com/paper/2106.10853/) (2021)
- [Learning to Play No-Press Diplomacy with Best Response Policy Iteration](https://mlsanity.com/paper/2006.04635/) (2020)
- [Improving Coordination in Small-Scale Multi-Agent Deep Reinforcement Learning through Memory-driven Communication](https://mlsanity.com/paper/1901.03887/) (2019)
- [Using Unity to Help Solve Intelligence](https://mlsanity.com/paper/2011.09294/) (2020)
- [Wildfire Monitoring in Remote Areas using Autonomous Unmanned Aerial Vehicles](https://mlsanity.com/paper/1905.00492/) (2019)
- [The Power of Communication in a Distributed Multi-Agent System](https://mlsanity.com/paper/2111.15611/) (2021)
- [Optimal algebraic Breadth-First Search for sparse graphs](https://mlsanity.com/paper/1906.03113/) (2019)
- [Collaborative Auto-Curricula Multi-Agent Reinforcement Learning with Graph Neural Network Communication Layer for Open-ended Wildfire-Management Resource Distribution](https://mlsanity.com/paper/2204.11350/) (2022)
- [Image-based Guidance of Autonomous Aircraft for Wildfire Surveillance and Prediction](https://mlsanity.com/paper/1810.02455/) (2018)

## Built on by (most cited first, 5 of 5)

- [PIMbot: Policy and Incentive Manipulation for Multi-Robot Reinforcement Learning in Social Dilemmas](https://mlsanity.com/paper/2307.15944/) (2023)
- [PIMbot: A Self-Adaptive Attack Framework for Adversarial Manipulation of Multi-Robot Reinforcement Learning](https://mlsanity.com/paper/2605.23027/) (2026)
- [Learning to Communicate and Collaborate in a Competitive Multi-Agent Setup to Clean the Ocean from Macroplastics](https://mlsanity.com/paper/2304.05872/) (2023)
- [Achieving Collective Welfare in Multi-Agent Reinforcement Learning via Suggestion Sharing](https://mlsanity.com/paper/2412.12326/) (2024)
- [OnDeFog: Online Decision Transformer under Frame Dropping](https://mlsanity.com/paper/2606.19721/) (2026)

## Abstract (arXiv)

We approach autonomous drone-based reforestation with a collaborative multi-agent reinforcement learning (MARL) setup. Agents can communicate as part of a dynamically changing network. We explore collaboration and communication on the back of a high-impact problem. Forests are the main resource to control rising CO2 conditions. Unfortunately, the global forest volume is decreasing at an unprecedented rate. Many areas are too large and hard to traverse to plant new trees. To efficiently cover as much area as possible, here we propose a Graph Neural Network (GNN) based communication mechanism that enables collaboration. Agents can share location information on areas needing reforestation, which increases viewed area and planted tree count. We compare our proposed communication mechanism with a multi-agent baseline without the ability to communicate. Results show how communication enables collaboration and increases collective performance, planting precision and the risk-taking propensity of individual agents.