mlsanityData to 7 Oct 2026

Multi-agent Soft Actor-Critic Based Hybrid Motion Planner for Mobile Robots

arXiv 2112.06594 ↗First posted 13 Dec 2021cs.RO cs.AIcited by 4

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Abstract (arXiv)

In this paper, a novel hybrid multi-robot motion planner that can be applied under non-communication and local observable conditions is presented. The planner is model-free and can realize the end-to-end mapping of multi-robot state and observation information to final smooth and continuous trajectories. The planner is a front-end and back-end separated architecture. The design of the front-end collaborative waypoints searching module is based on the multi-agent soft actor-critic algorithm under the centralized training with decentralized execution diagram. The design of the back-end trajectory optimization module is based on the minimal snap method with safety zone constraints. This module can output the final dynamic-feasible and executable trajectories. Finally, multi-group experimental results verify the effectiveness of the proposed motion planner.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 14 of its 35 references that are in the map. First posted on arXiv 13 Dec 2021.

Builds on

14 of 35 references.

References · 14

  1. 2017Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environmentscited by 1,519
  2. 2021The Surprising Effectiveness of PPO in Cooperative, Multi-Agent Gamescited by 903
  3. 2017Reinforcement Learning with Deep Energy-Based Policiescited by 659
  4. 2018Actor-Attention-Critic for Multi-Agent Reinforcement Learningcited by 259
  5. 2017Socially Aware Motion Planning with Deep Reinforcement Learningcited by 128
  6. 2016Decentralized Non-communicating Multiagent Collision Avoidance with Deep Reinforcement Learningcited by 104
  7. 2017Towards Optimally Decentralized Multi-Robot Collision Avoidance via Deep Reinforcement Learningcited by 81
  8. 2019Motion Planning Networks: Bridging the Gap Between Learning-based and Classical Motion Plannerscited by 62
  9. 2019Collision Avoidance in Pedestrian-Rich Environments with Deep Reinforcement Learningcited by 44
  10. 2021Modular Deep Reinforcement Learning for Continuous Motion Planning with Temporal Logiccited by 38
  11. 2020Multi-agent Motion Planning for Dense and Dynamic Environments via Deep Reinforcement Learningcited by 21
  12. 2020GLAS: Global-to-Local Safe Autonomy Synthesis for Multi-Robot Motion Planning with End-to-End Learningcited by 21
2 more
  1. 2015Optimal Multi-Robot Path Planning on Graphs: Complete Algorithms and Effective Heuristicscited by 12
  2. 2021A review of mobile robot motion planning methods: from classical motion planning workflows to reinforcement learning-based architecturescited by 11

Built on by

Cited by year

Citing papers per year: 2022: 2; 2023: 0; 2024: 0; 2025: 2.

4 papers.

  1. 2022Distributed Reinforcement Learning for Robot Teams: A Reviewcited by 11
  2. 2025Hybrid Action Based Reinforcement Learning for Multi-Objective Compatible Autonomous Drivingcited by 7
  3. 2022Multi-robot Social-aware Cooperative Planning in Pedestrian Environments Using Multi-agent Reinforcement Learningcited by 0
  4. 2025Intrinsic-Motivation Multi-Robot Social Formation Navigation with Coordinated Explorationcited by 0