Cooperative Multi-Agent Learning in a Complex World: Challenges and Solutions

Over the past few years, artificial intelligence (AI) has achieved great success in a variety of applications, such as image classification and recommendation systems. This often been by training machine learning models on static datasets, where inputs desired outputs are provided. However, we now seeing shift this paradigm. Instead from increasingly being trained through feedback their interactions with world. is particularly important when deployed real world, decisions can have an impact other agents, turning decision-making process into multi-agent problem. As result, complex environments critical area research for next generation AI, context cooperative tasks. Cooperative essential problem practitioners to consider it potential enable wide range In presentation, will review background challenges learning, survey our that aims address these challenges.