Multi-Agent Reinforcement Learning Needs More Than Better Rewards
SMRTR summary
Multi-agent reinforcement learning works well in controlled demos but fails in real-world applications because current benchmarks don't test practical requirements like staged planning, selective communication, and external safety constraints.
SMRTR provides this summary for quick context. The original article belongs to Hacker Noon.
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