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Toward Policy Explanations For Multi-Agent Reinforcement Learning by albertjin67 is a document available to read on EtoBox.

What is Toward Policy Explanations For Multi-Agent Reinforcement Learning about?

The document discusses novel methods for generating policy explanations in multi-agent reinforcement learning (MARL), focusing on policy summarization and query-based language explanations. These methods utilize an abstract representation of MARL policy as a multi-agent Markov decision process (MMDP) to improve transparency and user satisfaction. Experimental results demonstrate the effectiveness of these methods in enhancing user understanding of agent behavior across various MARL domains.

Author

albertjin67

Language

EN