• Corpus ID: 243848161

Variational Automatic Curriculum Learning for Sparse-Reward Cooperative Multi-Agent Problems

@inproceedings{Chen2021VariationalAC,
  title={Variational Automatic Curriculum Learning for Sparse-Reward Cooperative Multi-Agent Problems},
  author={Jiayu Chen and Yuanxin Zhang and Yuanfan Xu and Huimin Ma and Huazhong Yang and Jiaming Song and Yu Wang and Yi Wu},
  booktitle={NeurIPS},
  year={2021}
}
We introduce a curriculum learning algorithm, Variational Automatic Curriculum Learning (VACL), for solving challenging goal-conditioned cooperative multiagent reinforcement learning problems. We motivate our paradigm through a variational perspective, where the learning objective can be decomposed into two terms: task learning on the current task distribution, and curriculum update to a new task distribution. Local optimization over the second term suggests that the curriculum should gradually… 
1 Citations
Self-Paced Multi-Agent Reinforcement Learning
Curriculum reinforcement learning (CRL) aims to speed up learning of a task by changing gradually the difficulty of the task from easy to hard through control of factors such as initial state or

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