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Settling Decentralized Multi-Agent Coordinated Exploration by Novelty Sharing
The other is how agents can explore in a coordinated way.
Model-Based Opponent Modeling
When one agent interacts with a multi-agent environment, it is challenging to deal with various opponents unseen before.
Metric Policy Representations for Opponent Modeling
In multi-agent reinforcement learning, the inherent non-stationarity of the environment caused by other agents' actions posed significant difficulties for an agent to learn a good policy independently.
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