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Knowledge Reuse of Multi-Agent Reinforcement Learning in Cooperative Tasks
Daming Shi1, Junbo Tong1, Yi Liu1
1Department of Automation, Tsinghua University, Beijing 100084, China.
Entropy (Basel, Switzerland)
|April 23, 2022
Summary
This study introduces knowledge reuse methods for multi-agent reinforcement learning (MARL) to reduce training waste. By sharing experience and policies, agents train faster and new teammates integrate seamlessly, improving cooperative task performance.
Area of Science:
- Artificial Intelligence
- Machine Learning
Background:
- Multi-agent cooperation is crucial in artificial intelligence.
- Multi-agent reinforcement learning (MARL) is effective for cooperative tasks.
- Traditional MARL faces challenges with sample complexity, leading to training inefficiencies and difficulties in agent addition.
Purpose of the Study:
- To propose knowledge reuse methods for MARL to address training waste.
- To mitigate repetitive training of homogeneous agents.
- To enable efficient integration of new agents into existing teams.
Main Methods:
- Sharing experience and policies among agents.
- Reusing previously learned policies for new agent integration.
Main Results:
- Sharing experience and policies accelerates training and improves performance in cooperative tasks.
- Transferring learned policies allows new agent teams to perform tasks successfully with minimal additional training.
- The proposed methods achieve optimal performance comparable to training from scratch.
Conclusions:
- Knowledge reuse methods significantly reduce training waste in MARL.
- The approach enhances training efficiency and performance in cooperative multi-agent systems.
- This method facilitates scalable and adaptable multi-agent system development.
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