# Mobile Robot Path Planning in Dynamic Environments through Globally Guided Reinforcement Learning

arXiv: https://arxiv.org/abs/2005.05420 · HTML: https://mlsanity.com/paper/2005.05420/

## Key facts

- Cited by 37 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 May 2020.

## Built on by (most cited first, 30 of 37)

- [VMAS: A Vectorized Multi-Agent Simulator for Collective Robot Learning](https://mlsanity.com/paper/2207.03530/) (2022)
- [Distributed Heuristic Multi-Agent Path Finding with Communication](https://mlsanity.com/paper/2106.11365/) (2021)
- [Learning Selective Communication for Multi-Agent Path Finding](https://mlsanity.com/paper/2109.05413/) (2021)
- [Multi-Agent Reinforcement Learning for Autonomous Driving: A Survey](https://mlsanity.com/paper/2408.09675/) (2024)
- [POGEMA: A Benchmark Platform for Cooperative Multi-Agent Pathfinding](https://mlsanity.com/paper/2407.14931/) (2024)
- [Beyond Robustness: A Taxonomy of Approaches towards Resilient Multi-Robot Systems](https://mlsanity.com/paper/2109.12343/) (2021)
- [Decentralized Monte Carlo Tree Search for Partially Observable Multi-agent Pathfinding](https://mlsanity.com/paper/2312.15908/) (2023)
- [A Framework for Real-World Multi-Robot Systems Running Decentralized GNN-Based Policies](https://mlsanity.com/paper/2111.01777/) (2021)
- [Graph-Based Multi-Robot Path Finding and Planning](https://mlsanity.com/paper/2206.11319/) (2022)
- [Deploying Ten Thousand Robots: Scalable Imitation Learning for Lifelong Multi-Agent Path Finding](https://mlsanity.com/paper/2410.21415/) (2024)
- [Learning Team-Based Navigation: A Review of Deep Reinforcement Learning Techniques for Multi-Agent Pathfinding](https://mlsanity.com/paper/2308.05893/) (2023)
- [Efficient Episodic Memory Utilization of Cooperative Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2403.01112/) (2024)
- [The Holy Grail of Multi-Robot Planning: Learning to Generate Online-Scalable Solutions from Offline-Optimal Experts](https://mlsanity.com/paper/2107.12254/) (2021)
- [SIGMA: Sheaf-Informed Geometric Multi-Agent Pathfinding](https://mlsanity.com/paper/2502.06440/) (2025)
- [DeepFleet: Multi-Agent Foundation Models for Mobile Robots](https://mlsanity.com/paper/2508.08574/) (2025)
- [Pairwise is Not Enough: Hypergraph Neural Networks for Multi-Agent Pathfinding](https://mlsanity.com/paper/2602.06733/) (2026)
- [Optimal Solving of Constrained Path-Planning Problems with Graph Convolutional Networks and Optimized Tree Search](https://mlsanity.com/paper/2108.01036/) (2021)
- [Accelerating Multi-Agent Planning Using Graph Transformers with Bounded Suboptimality](https://mlsanity.com/paper/2301.08451/) (2023)
- [Exploiting Hybrid Policy in Reinforcement Learning for Interpretable Temporal Logic Manipulation](https://mlsanity.com/paper/2412.20338/) (2024)
- [CAMAR: Continuous Actions Multi-Agent Routing](https://mlsanity.com/paper/2508.12845/) (2025)
- [SutureFormer: Learning Surgical Trajectories via Goal-conditioned Offline RL in Pixel Space](https://mlsanity.com/paper/2603.26720/) (2026)
- [OctoPath: An OcTree Based Self-Supervised Learning Approach to Local Trajectory Planning for Mobile Robots](https://mlsanity.com/paper/2106.00988/) (2021)
- [Dyna-LfLH: Learning Agile Navigation in Dynamic Environments from Learned Hallucination](https://mlsanity.com/paper/2403.17231/) (2024)
- [MFC-EQ: Mean-Field Control with Envelope Q-Learning for Moving Decentralized Agents in Formation](https://mlsanity.com/paper/2410.12062/) (2024)
- [LLMDR: LLM-Driven Deadlock Detection and Resolution in Multi-Agent Pathfinding](https://mlsanity.com/paper/2503.00717/) (2025)
- [Stochastic Path Planning in Correlated Obstacle Fields](https://mlsanity.com/paper/2509.19559/) (2025)
- [Hybrid Motion Planning with Deep Reinforcement Learning for Mobile Robot Navigation](https://mlsanity.com/paper/2512.24651/) (2025)
- [A Fully Controllable Agent in the Path Planning using Goal-Conditioned Reinforcement Learning](https://mlsanity.com/paper/2205.09967/) (2022)
- [Multi-robot Social-aware Cooperative Planning in Pedestrian Environments Using Multi-agent Reinforcement Learning](https://mlsanity.com/paper/2211.15901/) (2022)
- [Multi-Robot Path Planning Combining Heuristics and Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2306.01270/) (2023)

## Abstract (arXiv)

Path planning for mobile robots in large dynamic environments is a challenging problem, as the robots are required to efficiently reach their given goals while simultaneously avoiding potential conflicts with other robots or dynamic objects. In the presence of dynamic obstacles, traditional solutions usually employ re-planning strategies, which re-call a planning algorithm to search for an alternative path whenever the robot encounters a conflict. However, such re-planning strategies often cause unnecessary detours. To address this issue, we propose a learning-based technique that exploits environmental spatio-temporal information. Different from existing learning-based methods, we introduce a globally guided reinforcement learning approach (G2RL), which incorporates a novel reward structure that generalizes to arbitrary environments. We apply G2RL to solve the multi-robot path planning problem in a fully distributed reactive manner. We evaluate our method across different map types, obstacle densities, and the number of robots. Experimental results show that G2RL generalizes well, outperforming existing distributed methods, and performing very similarly to fully centralized state-of-the-art benchmarks.