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zhang23a.pdf
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🔗 Source: proceedings.mlr.press
📊 Size: 1.38 MB
📄 Pages: 12 pages
⬇️ Downloads: 76

Summary

Cooperative multi-agent reinforcement learning (MARL) may suffer from non-stationarity due to teammates' policy changes, which can lead to miscoordination and poor performance. Existing works mainly focus on policy changes across episodes, ignoring sudden policy changes within an episode. This work formulates the problem as an open Dec-POMDP and develops a new framework, Fastap, to address the issue. Fastap trains versatile teammates' policies, assigns them to clusters via the Chinese Restaurant Process, and trains controlled agents to coordinate with sampled teammates by capturing their identifications as context for fast adaptation. Experimental results show that Fastap can achieve superior performance in stationary and non-stationary scenarios compared to multiple baselines.

Description

Cooperative multi-agent reinforcement learning (MARL) may suffer from non-stationarity due to teammates' policy changes, which can lead to miscoordination and...

Technical Information

  • File Format: PDF
  • File Size: 1.38 MB
  • Pages: 12
  • Language: EN
  • Total Downloads: 76
  • Last Updated: 6 days ago

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