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OpenAI researchers demonstrate that cooperative AI agents can develop their own grounded and compositional language through reinforcement learning in simple worlds. The agents learn to communicate by being rewarded for achieving goals that require coordination, creating shared symbolic languages to coordinate behavior.
OpenAI researchers propose a multi-agent learning environment where agents develop a grounded compositional language with defined vocabulary and syntax to achieve goals. The study also observes emergence of non-verbal communication like pointing when language is unavailable.