Efficient Distributed Framework for Collaborative Multi-Agent Reinforcement Learning

2022-05-11 · Shuhan Qi, Shuhao Zhang, Xiaohan Hou, Jiajia Zhang, Xuan Wang, Jing Xiao

Multi-agent reinforcement learning for incomplete information environments has attracted extensive attention from researchers. However, due to the slow sample collection and poor sample exploration, there are still some problems in multi-agent reinforcement learning, such as unstable model iteration and low training efficiency. Moreover, most of the existing distributed framework are proposed for single-agent reinforcement learning and not suitable for multi-agent. In this paper, we design an distributed MARL framework based on the actor-work-learner architecture. In this framework, multiple asynchronous environment interaction modules can be deployed simultaneously, which greatly improves the sample collection speed and sample diversity. Meanwhile, to make full use of computing resources, we decouple the model iteration from environment interaction, and thus accelerate the policy iteration. Finally, we verified the effectiveness of propose framework in MaCA military simulation environment and the SMAC 3D realtime strategy gaming environment with imcomplete information characteristics.

📄 PDF Abstract BibTeX arXiv:2205.05248

Code (0)

등록된 구현이 없습니다.

Tasks

DiversityMulti-agent Reinforcement Learning reinforcement-learning Reinforcement Learning Reinforcement Learning (RL)SMAC SMAC+

Methods 이 논문이 사용한 방법론

SPEED The monocular depth estimation (MDE) is the task of estimating depth from a single frame. This information is an essential knowledge in many computer vision tasks such as scene…

Towards Closing the Sim-to-Real Gap in Collaborative Multi-Robot Deep Reinforcement Learning

2020-08-18 · Wenshuai Zhao, Jorge Peña Queralta, Li Qingqing, Tomi Westerlund

Current research directions in deep reinforcement learning include bridging the simulation-reality gap, improving sample efficiency of experiences in distributed multi-agent reinforcement learning, together with the deve…

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

Ubiquitous Distributed Deep Reinforcement Learning at the Edge: Analyzing Byzantine Agents in Discrete Action Spaces

2020-08-18 · Wenshuai Zhao, Jorge Peña Queralta, Li Qingqing, Tomi Westerlund

The integration of edge computing in next-generation mobile networks is bringing low-latency and high-bandwidth ubiquitous connectivity to a myriad of cyber-physical systems. This will further boost the increasing intell…

Deep Reinforcement Learning Edge-computing

Collaborative Multi-Agent Video Fast-Forwarding

2023-05-27 · Shuyue Lan, Zhilu Wang, Ermin Wei, Amit K. Roy-Chowdhury 외

Multi-agent applications have recently gained significant popularity. In many computer vision tasks, a network of agents, such as a team of robots with cameras, could work collaboratively to perceive the environment for …

Distributed Transmission Control for Wireless Networks using Multi-Agent Reinforcement Learning

2022-05-13 · Collin Farquhar, Prem Sagar Pattanshetty Vasanth Kumar, Anu Jagannath, Jithin Jagannath

We examine the problem of transmission control, i.e., when to transmit, in distributed wireless communications networks through the lens of multi-agent reinforcement learning. Most other works using reinforcement learnin…

Multi-agent Reinforcement Learning reinforcement-learning Reinforcement Learning Reinforcement Learning (RL)

Agent-Time Attention for Sparse Rewards Multi-Agent Reinforcement Learning

2022-10-31 · Jennifer She, Jayesh K. Gupta, Mykel J. Kochenderfer

Sparse and delayed rewards pose a challenge to single agent reinforcement learning. This challenge is amplified in multi-agent reinforcement learning (MARL) where credit assignment of these rewards needs to happen not on…