playing-games-15
Multi-agent Reinforcement Learning
3260 papers • 126 benchmarks • 313 datasets
The target of Multi-agent Reinforcement Learning is to solve complex problems by integrating multiple agents that focus on different sub-tasks. In general, there are two types of multi-agent systems: independent and cooperative systems. Source: Show, Describe and Conclude: On Exploiting the Structure Information of Chest X-Ray Reports
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Benchmarks
These leaderboards are used to track progress in multi-agent-reinforcement-learning-31
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ParticleEnvs Cooperative Communication
ParticleEnvs Cooperative Communication
SMAC-Exp
SMAC-Exp
Libraries
iUse these libraries to find multi-agent-reinforcement-learning-31 models and implementations
6 papers 234
