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Arxiv
4d
16
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The Problem of Social Cost in Multi-Agent General Reinforcement Learning: Survey and Synthesis
- This paper discusses the problem of social harms in multi-agent reinforcement learning.
- It proposes market-based mechanisms to measure and control the cost of social harms.
- The setup captures a wide range of scenarios and allows for different learning strategies.
- It provides practical applications, such as the Paperclips problem and pollution control.
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