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- Deep Reinforcement Learning-Based Large-Scale Robot Exploration64
This work proposes a deep reinforcement learning (DRL) based reactive planner to solve large-scale Lidar-based autonomous robot exploration problems in 2D action space that relies on ground truth information and a graph rarefaction algorithm, which allows models trained in small-scale environments to scale to large-scale ones.
- MARVEL: Multi-Agent Reinforcement Learning for Constrained Field-of-View Multi-Robot Exploration in Large-Scale Environments25
This work proposes MARVEL, a neural framework that leverages graph attention networks, together with novel frontiers and orientation features fusion technique, to develop a collaborative, decentralized policy using multi-agent reinforcement learning (MARL) for robots with constrained FoV, and introduces a novel information-driven action pruning strategy.
- HDPlanner: Advancing Autonomous Deployments in Unknown Environments Through Hierarchical Decision Networks21
This paper empirically demonstrates that HDPlanner significantly outperforms state-of-the-art conventional and learning-based baselines on an extensive set of simulations, including hundreds of test maps and large-scale, complex Gazebo environments, and proposes a contrastive learning-based joint optimization to enhance the robustness of HDPlanner.
- ORION: Option-Regularized Deep Reinforcement Learning for Cooperative Multi-Agent Online Navigation2
This work designs a shared graph encoder that fuses prior map with online perception into a unified representation, providing robust state embeddings under environmental discrepancies, and introduces a dual-stage cooperation strategy that allows agents to assist teammates under map uncertainty, thereby reducing the overall makespan.
- COIN: Collaborative interaction-aware multiagent reinforcement learning for self-driving systems–
This work develops a new counterfactual individual-global twin delayed deep deterministic policy gradient (CIG-TD3) algorithm, crafted in a “centralized training, decentralized execution” (CTDE) manner, which aims to jointly optimize the individual objectives (navigation) and the global objectives (collaboration) of agents.
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Publication data from OpenAlex, with missing venues and authors filled in from Crossref; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-11. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
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