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

2021-12-13 · Zichen He, Lu Dong, Chunwei Song, Changyin Sun

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.

📄 PDF Abstract BibTeX arXiv:2112.06594

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Deep Multi-Agent Reinforcement Learning with Hybrid Action Spaces based on Maximum Entropy

2022-06-10 · Hongzhi Hua, Kaigui Wu, Guixuan Wen

Multi-agent deep reinforcement learning has been applied to address a variety of complex problems with either discrete or continuous action spaces and achieved great success. However, most real-world environments cannot …

Deep Reinforcement Learning Multi-agent Reinforcement Learning reinforcement-learning Reinforcement Learning+1

A further exploration of deep Multi-Agent Reinforcement Learning with Hybrid Action Space

2022-08-30 · Hongzhi Hua, Guixuan Wen, Kaigui Wu

The research of extending deep reinforcement learning (drl) to multi-agent field has solved many complicated problems and made great achievements. However, almost all these studies only focus on discrete or continuous ac…

Deep Reinforcement Learning Multi-agent Reinforcement Learning reinforcement-learning Reinforcement Learning+1

Hybrid Multi-agent Deep Reinforcement Learning for Autonomous Mobility on Demand Systems

2022-12-14 · Tobias Enders, James Harrison, Marco Pavone, Maximilian Schiffer

We consider the sequential decision-making problem of making proactive request assignment and rejection decisions for a profit-maximizing operator of an autonomous mobility on demand system. We formalize this problem as …

Decision Making Deep Reinforcement Learning reinforcement-learning Reinforcement Learning (RL)+1

Sequence Aware SAC Control for Engine Fuel Consumption Optimization in Electrified Powertrain

2025-08-06 · Wafeeq Jaleel, Md Ragib Rownak, Athar Hanif, Sidra Ghayour Bhatti 외 arxiv

As hybrid electric vehicles (HEVs) gain traction in heavy-duty trucks, adaptive and efficient energy management is critical for reducing fuel consumption while maintaining battery charge for long operation times. We pres…

Reinforcement Learning

SACHA: Soft Actor-Critic with Heuristic-Based Attention for Partially Observable Multi-Agent Path Finding

2023-07-05 · Qiushi Lin, Hang Ma

Multi-Agent Path Finding (MAPF) is a crucial component for many large-scale robotic systems, where agents must plan their collision-free paths to their given goal positions. Recently, multi-agent reinforcement learning h…

Multi-Agent Path Finding Multi-agent Reinforcement Learning