Double Distillation Network for Multi-Agent Reinforcement Learning

2025-02-05 · Yang Zhou, Siying Wang, Wenyu Chen, Ruoning Zhang, Zhitong Zhao, Zixuan Zhang

Multi-agent reinforcement learning typically employs a centralized training-decentralized execution (CTDE) framework to alleviate the non-stationarity in environment. However, the partial observability during execution may lead to cumulative gap errors gathered by agents, impairing the training of effective collaborative policies. To overcome this challenge, we introduce the Double Distillation Network (DDN), which incorporates two distillation modules aimed at enhancing robust coordination and facilitating the collaboration process under constrained information. The external distillation module uses a global guiding network and a local policy network, employing distillation to reconcile the gap between global training and local execution. In addition, the internal distillation module introduces intrinsic rewards, drawn from state information, to enhance the exploration capabilities of agents. Extensive experiments demonstrate that DDN significantly improves performance across multiple scenarios.

📄 PDF Abstract BibTeX arXiv:2502.03125

Code (0)

등록된 구현이 없습니다.

Tasks

Multi-agent Reinforcement Learning reinforcement-learning Reinforcement Learning

Weighted Double Deep Multiagent Reinforcement Learning in Stochastic Cooperative Environments

2018-02-23 · Yan Zheng, Jianye Hao, Zongzhang Zhang

Recently, multiagent deep reinforcement learning (DRL) has received increasingly wide attention. Existing multiagent DRL algorithms are inefficient when facing with the non-stationarity due to agents update their policie…

Deep Reinforcement Learning Q-Learning reinforcement-learning Reinforcement Learning+1

Heterogeneous Agent Collaborative Reinforcement Learning

2026-03-03 · Zhixia Zhang, Zixuan Huang, Gongxun Li, Huaiyang Wang 외 arxiv

We introduce Heterogeneous Agent Collaborative Reinforcement Learning (HACRL), a new Reinforcement Learning from Verifiable Reward (RLVR) problem that addresses the inefficiencies of isolated multi-agent on-policy optimi…

Multi-agent Reinforcement Learning

Multi-Agent Reinforcement Learning via Double Averaging Primal-Dual Optimization

2018-06-03 · NeurIPS 2018 12 · Hoi-To Wai, Zhuoran Yang, Zhaoran Wang, Mingyi Hong

Despite the success of single-agent reinforcement learning, multi-agent reinforcement learning (MARL) remains challenging due to complex interactions between agents. Motivated by decentralized applications such as sensor…

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

Policy Distillation and Value Matching in Multiagent Reinforcement Learning

2019-03-15 · Samir Wadhwania, Dong-Ki Kim, Shayegan Omidshafiei, Jonathan P. How

Multiagent reinforcement learning algorithms (MARL) have been demonstrated on complex tasks that require the coordination of a team of multiple agents to complete. Existing works have focused on sharing information betwe…

reinforcement-learning Reinforcement Learning Reinforcement Learning (RL)

Double A3C: Deep Reinforcement Learning on OpenAI Gym Games

2023-03-04 · Yangxin Zhong, Jiajie He, Lingjie Kong

Reinforcement Learning (RL) is an area of machine learning figuring out how agents take actions in an unknown environment to maximize its rewards. Unlike classical Markov Decision Process (MDP) in which agent has full kn…

Atari Games Deep Reinforcement Learning Efficient Neural Network OpenAI Gym+4