mlsanityData to 14 Oct 2026

Privileged Reinforcement and Communication Learning for Distributed, Bandwidth-limited Multi-robot Exploration

arXiv 2407.20203 ↗First posted 29 Jul 2024cs.ROcited by 0

On the map: Deep reinforcement learning methods → RL for robot navigation and UAV control

Abstract (arXiv)

Communication bandwidth is an important consideration in multi-robot exploration, where information exchange among robots is critical. While existing methods typically aim to reduce communication throughput, they either require significant computation or significantly compromise exploration efficiency. In this work, we propose a deep reinforcement learning framework based on communication and privileged reinforcement learning to achieve a significant reduction in bandwidth consumption, while minimally sacrificing exploration efficiency. Specifically, our approach allows robots to learn to embed the most salient information from their individual belief (partial map) over the environment into fixed-sized messages. Robots then reason about their own belief as well as received messages to distributedly explore the environment while avoiding redundant work. In doing so, we employ privileged learning and learned attention mechanisms to endow the critic (i.e., teacher) network with ground truth map knowledge to effectively guide the policy (i.e., student) network during training. Compared to relevant baselines, our model allows the team to reduce communication by up to two orders of magnitude, while only sacrificing a marginal 2.4% in total travel distance, paving the way for efficient, distributed multi-robot exploration in bandwidth-limited scenarios.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in RL for robot navigation and UAV control, one of 15 directions in Deep reinforcement learning methods. Builds on 11 of its 31 references that are in the map. First posted on arXiv 29 Jul 2024.

Builds on

11 of 31 references.

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Same direction, newest first

  1. 2024Deep Reinforcement Learning-based Large-scale Robot Exploration16 Mar

Other references · 10

  1. 2017Attention Is All You Needcited by 60,361
  2. 2018Soft Actor-Critic Algorithms and Applicationscited by 1,240
  3. 2016Learning to Communicate with Deep Multi-Agent Reinforcement Learningcited by 575
  4. 2017Ray: A Distributed Framework for Emerging AI Applicationscited by 527
  5. 2019Soft Actor-Critic for Discrete Action Settingscited by 174
  6. 2017Multiagent Bidirectionally-Coordinated Nets: Emergence of Human-level Coordination in Learning to Play StarCraft Combat Gamescited by 96
  7. 2019Learning to Schedule Communication in Multi-agent Reinforcement Learningcited by 93
  8. 2023Asynchronous Multi-Agent Reinforcement Learning for Efficient Real-Time Multi-Robot Cooperative Explorationcited by 18
  9. 2023ARiADNE: A Reinforcement learning approach using Attention-based Deep Networks for Explorationcited by 18
  10. 2022Distributed Reinforcement Learning for Robot Teams: A Reviewcited by 16

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  2. 2025HEADER: Hierarchical Robot Exploration via Attention-Based Deep Reinforcement Learning with Expert-Guided Reward
  3. 2024Reinforcement Learning Driven Multi-Robot Exploration via Explicit Communication and Density-Based Frontier Search
  4. 2026Bandwidth-Efficient Multi-Agent Communication through Information Bottleneck and Vector Quantization
  5. 2024Scalable Multi-Robot Informative Path Planning for Target Mapping via Deep Reinforcement Learning
  6. 2024Investigating the Impact of Communication-Induced Action Space on Exploration of Unknown Environments with Decentralized Multi-Agent Reinforcement Learning ↗
  7. 2026Robust and Efficient Communication for Multi-Agent Learning
  8. 2024An Enhanced Hierarchical Planning Framework for Multi-Robot Autonomous Exploration