Cautiously-optimistic Knowledge Sharing For Cooperative Multi-agent Reinforcement Learning

Abstract

While decentralized training is attractive in multi-agent reinforcement learning (MARL) for its excellent scalability and robustness, its inherent coordination challenges in collaborative tasks result in numerous interactions for agents to learn good policies. To alleviate this problem, action advising methods make experienced agents share their knowledge about what to do, while less experienced agents strictly follow the received advice. However, this method of sharing and utilizing knowledge may hinder the team's exploration of better states, as agents can be unduly influenced by suboptimal or even adverse advice, especially in the early stages of learning. Inspired by the fact that humans can learn not only from the success but also from the failure of others, this paper proposes a novel knowledge sharing framework called Cautiously-Optimistic kNowledge Sharing (CONS). CONS enables each agent to share both positive and negative knowledge and cautiously assimilate knowledge from othe

Stats

• citations5
• S2 citations—
• github stars0
• HF likes0
• heat score5.84
• arxiv keyba2023cautiously

Related papers

• Contextual Knowledge Sharing In Multi-agent Reinforcement Learning With Decentralized Communication And Coordination (2025)0.00
• Knowsr: Knowledge Sharing Among Homogeneous Agents In Multi-agent Reinforcement Learning (2021)0.00
• Achieving Collective Welfare In Multi-agent Reinforcement Learning Via Suggestion Sharing (2024)0.00
• Learning To Share In Multi-agent Reinforcement Learning (2021)0.00
• Multi-agent Common Knowledge Reinforcement Learning (2018)0.00
• Tacit Learning With Adaptive Information Selection For Cooperative Multi-agent Reinforcement Learning (2024)0.00
• Toward Risk-based Optimistic Exploration For Cooperative Multi-agent Reinforcement Learning (2023)0.00
• Learning To Advise And Learning From Advice In Cooperative Multi-agent Reinforcement Learning (2022)0.00