Message-Dropout: An Efficient Training Method for Multi-Agent Deep Reinforcement Learning

Kim, Woojun; Cho, Myung-sik; Sung, Youngchul

doi:10.48550/arxiv.1902.06527

Cited by 1 publication

(2 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The method [43] is also based on the top of MADDPG. They introduce dropout technique [44] into MADDPG algorithm and proposed Message-Dropout Multi-Agent Deep Deterministic Policy Gradient (MADDPG-MD).…”

Section: Learning Coordinationmentioning

confidence: 99%

See 1 more Smart Citation

Cooperative and competitive multi-agent deep reinforcement learning

Chen

2022

2nd International Conference on Artificial Intelligence, Automation, and High-Performance Computing (AIAHPC 2022)

View full text Add to dashboard Cite

Multi-agent reinforcement learning (MARL) is an area of artificial intelligence that investigates joint behaviors of multiple individual agents and emergent patterns arising from their interactions with a common environment. Although MARL has a long history of decades, it begins to intensify recently due to the breakthrough of deep learning methods. In recent years Deep reinforcement learning (DRL) has achieved significant progress in single-agent reinforcement learning problems. Meanwhile, multi-agent systems (MASs) also benefit a lot from DRL methods. Latest advances occur in areas including video games, robot system, smart grids, etc. This article mostly focuses on recent papers on Multi-agent deep reinforcement learning (MADRL). First, some background knowledge of DRL and MARL is introduced. Both value-based and policy-based DRL algorithms are discussed. Second, representative works in both cooperative and competitive scenarios are reviewed respectively. Key ideas and main techniques in each work are discussed. Lastly, the paper draws a conclusion and some potential research directions are proposed.

show abstract

Section: Learning Coordinationmentioning

confidence: 99%

“…As mentioned in Section III(B), some researches [42,43] allow agents to share information through communication. Learning communication is another intensifying field in MARL area.…”

Section: Learning Communicationmentioning

confidence: 99%

Cooperative and competitive multi-agent deep reinforcement learning

Chen

2022

2nd International Conference on Artificial Intelligence, Automation, and High-Performance Computing (AIAHPC 2022)

View full text Add to dashboard Cite

show abstract

Message-Dropout: An Efficient Training Method for Multi-Agent Deep Reinforcement Learning

Cited by 1 publication

References 7 publications

Cooperative and competitive multi-agent deep reinforcement learning

Cooperative and competitive multi-agent deep reinforcement learning

Contact Info

Product

Resources

About