
Agents can learn adaptive behavior in multi-agent systems by using gradient-based state representations (SocialGFs) learned from offline examples, which enable transfer across tasks and efficient credit assignment in sparse reward settings.


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learning-gradient-based-representations-for-adaptive-multi-agent-reinforcement-learning


Learning Gradient-based Representations for Adaptive Multi-Agent Reinforcement Learning
