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

arXiv: https://arxiv.org/abs/2112.06594 · HTML: https://mlsanity.com/paper/2112.06594/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 35 references that are in the map.
- First posted on arXiv 13 Dec 2021.

## Builds on (most cited first, 14 of 14)

- [Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments](https://mlsanity.com/paper/1706.02275/) (2017)
- [The Surprising Effectiveness of PPO in Cooperative, Multi-Agent Games](https://mlsanity.com/paper/2103.01955/) (2021)
- [Reinforcement Learning with Deep Energy-Based Policies](https://mlsanity.com/paper/1702.08165/) (2017)
- [Actor-Attention-Critic for Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/1810.02912/) (2018)
- [Socially Aware Motion Planning with Deep Reinforcement Learning](https://mlsanity.com/paper/1703.08862/) (2017)
- [Decentralized Non-communicating Multiagent Collision Avoidance with Deep Reinforcement Learning](https://mlsanity.com/paper/1609.07845/) (2016)
- [Towards Optimally Decentralized Multi-Robot Collision Avoidance via Deep Reinforcement Learning](https://mlsanity.com/paper/1709.10082/) (2017)
- [Motion Planning Networks: Bridging the Gap Between Learning-based and Classical Motion Planners](https://mlsanity.com/paper/1907.06013/) (2019)
- [Collision Avoidance in Pedestrian-Rich Environments with Deep Reinforcement Learning](https://mlsanity.com/paper/1910.11689/) (2019)
- [Modular Deep Reinforcement Learning for Continuous Motion Planning with Temporal Logic](https://mlsanity.com/paper/2102.12855/) (2021)
- [Multi-agent Motion Planning for Dense and Dynamic Environments via Deep Reinforcement Learning](https://mlsanity.com/paper/2001.06627/) (2020)
- [GLAS: Global-to-Local Safe Autonomy Synthesis for Multi-Robot Motion Planning with End-to-End Learning](https://mlsanity.com/paper/2002.11807/) (2020)
- [Optimal Multi-Robot Path Planning on Graphs: Complete Algorithms and Effective Heuristics](https://mlsanity.com/paper/1507.03290/) (2015)
- [A review of mobile robot motion planning methods: from classical motion planning workflows to reinforcement learning-based architectures](https://mlsanity.com/paper/2108.13619/) (2021)

## Built on by (most cited first, 5 of 5)

- [Distributed Reinforcement Learning for Robot Teams: A Review](https://mlsanity.com/paper/2204.03516/) (2022)
- [Hybrid Action Based Reinforcement Learning for Multi-Objective Compatible Autonomous Driving](https://mlsanity.com/paper/2501.08096/) (2025)
- [Effective Reinforcement Learning Control using Conservative Soft Actor-Critic](https://mlsanity.com/paper/2505.03356/) (2025)
- [Multi-robot Social-aware Cooperative Planning in Pedestrian Environments Using Multi-agent Reinforcement Learning](https://mlsanity.com/paper/2211.15901/) (2022)
- [Intrinsic-Motivation Multi-Robot Social Formation Navigation with Coordinated Exploration](https://mlsanity.com/paper/2512.13293/) (2025)

## 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.