• Corpus ID: 224897359

Randomized Entity-wise Factorization for Multi-Agent Reinforcement Learning

@inproceedings{Iqbal2021RandomizedEF,
  title={Randomized Entity-wise Factorization for Multi-Agent Reinforcement Learning},
  author={Shariq Iqbal and C. S. D. Witt and Bei Peng and Wendelin Bohmer and Shimon Whiteson and Fei Sha},
  booktitle={ICML},
  year={2021}
}
Real world multi-agent tasks often involve varying types and quantities of agents and non-agent entities; however, agents within these tasks rarely need to consider all others at all times in order to act effectively. Factored value function approaches have historically leveraged such independences to improve learning efficiency, but these approaches typically rely on domain knowledge to select fixed subsets of state features to include in each factor. We propose to utilize value function… 

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