How Modern Game Theory is Influencing Multi-Agent Reinforcement Learning Systems
Multi-Agent Reinforcement Learning(MARL)
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Multi-Agent Reinforcement Learning(MARL)
Last updated
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이런 것도 되는 구나 싶기도 하고, 이 정도는 되야지 싶기도 하다. 이런식이면 전략적 우월성도 자본과 기술력에 종속되겠구나 싶기도 하고, 어쩌면 고도화된 전략적 능력이 보편화될 수 있겠구나 싶기도 하다. 물론 갈길이 멀어 보이긴 한다. 이제까지와는 좀 다른 성격의 구조가 필요한 것 아닌가 싶기도 하고. 아무튼 가보자, 노란 벽돌길을 따라 에머랄드 씨티로~
Most artificial intelligence(AI) systems nowadays are based on a single agent tackling a task or, in the case of adversarial models, a couple of agents that compete against each other to improve the overall behavior of a system. However, many cognition problems in the real world are the result of knowledge built by large groups of people. Take for example a self-driving car scenario, the decisions of any agent are the result of the behavior of many other agents in the scenario. Many scenarios in financial markets or economics are also the result of coordinated actions between large groups of entities. How can we mimic that behavior in artificial intelligence(AI) agents?
