mlsanityData to 7 Oct 2026

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

arXiv 2211.15414 ↗First posted 14 Nov 2022cs.AI cs.LG cs.MAcited by 4

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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.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 30 of its 85 references that are in the map. First posted on arXiv 14 Nov 2022.

Builds on

30 of 85 references.

References · 30

  1. 2017Proximal Policy Optimization Algorithmscited by 14,441
  2. 2017Graph Attention Networkscited by 8,854
  3. 2017Neural Message Passing for Quantum Chemistrycited by 3,460
  4. 2016Convolutional Neural Networks on Graphs with Fast Localized Spectral Filteringcited by 2,085
  5. 2018Graph Neural Networks: A Review of Methods and Applicationscited by 1,571
  6. 2014DeepWalk: Online Learning of Social Representationscited by 1,543
  7. 2017Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environmentscited by 1,519
  8. 2018Relational inductive biases, deep learning, and graph networkscited by 1,341
  9. 2019Dota 2 with Large Scale Deep Reinforcement Learningcited by 1,084
  10. 2018IMPALA: Scalable Distributed Deep-RL with Importance Weighted Actor-Learner Architecturescited by 866
  11. 2015Gated Graph Sequence Neural Networkscited by 792
  12. 2016Learning to Communicate with Deep Multi-Agent Reinforcement Learningcited by 561
18 more
  1. 2016Safe, Multi-Agent, Reinforcement Learning for Autonomous Drivingcited by 301
  2. 2017Thinking Fast and Slow with Deep Learning and Tree Searchcited by 245
  3. 2020The NetHack Learning Environmentcited by 196
  4. 2021Open-Ended Learning Leads to Generally Capable Agentscited by 177
  5. 2021Scalable Evaluation of Multi-Agent Reinforcement Learning with Melting Potcited by 97
  6. 2017A Survey of Learning in Multiagent Environments: Dealing with Non-Stationaritycited by 93
  7. 2020Reinforcement Learning in Economics and Financecited by 57
  8. 2020Learning Individually Inferred Communication for Multi-Agent Cooperationcited by 55
  9. 2019Efficient Communication in Multi-Agent Reinforcement Learning via Variance Based Controlcited by 46
  10. 2021On the Importance of Environments in Human-Robot Coordinationcited by 37
  11. 2020Learning to Play No-Press Diplomacy with Best Response Policy Iterationcited by 25
  12. 2019Improving Coordination in Small-Scale Multi-Agent Deep Reinforcement Learning through Memory-driven Communicationcited by 18
  13. 2020Using Unity to Help Solve Intelligencecited by 13
  14. 2019Wildfire Monitoring in Remote Areas using Autonomous Unmanned Aerial Vehiclescited by 12
  15. 2021The Power of Communication in a Distributed Multi-Agent Systemcited by 4
  16. 2019Optimal algebraic Breadth-First Search for sparse graphscited by 3
  17. 2022Collaborative Auto-Curricula Multi-Agent Reinforcement Learning with Graph Neural Network Communication Layer for Open-ended Wildfire-Management Resource Distributioncited by 3
  18. 2018Image-based Guidance of Autonomous Aircraft for Wildfire Surveillance and Predictioncited by 2

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 2; 2024: 1; 2025: 0; 2026: 1.

4 papers.

  1. 2023PIMbot: Policy and Incentive Manipulation for Multi-Robot Reinforcement Learning in Social Dilemmascited by 4
  2. 2023Learning to Communicate and Collaborate in a Competitive Multi-Agent Setup to Clean the Ocean from Macroplasticscited by 0
  3. 2024Achieving Collective Welfare in Multi-Agent Reinforcement Learning via Suggestion Sharingcited by 0
  4. 2026OnDeFog: Online Decision Transformer under Frame Droppingcited by 0