methodology-4
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-8
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ParticleEnvs Cooperative Communication
ParticleEnvs Cooperative Communication
SMAC-Exp
SMAC-Exp
Libraries
iUse these libraries to find multi-agent-reinforcement-learning-8 models and implementations
6 papers 234
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Most implemented papers
Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments
P. Abbeel, Aviv Tamar, J. Harb, Yi Wu, Ryan Lowe, Igor Mordatch•Tue Jun 06 2017
An adaptation of actor-critic methods that considers action policies of other agents and is able to successfully learn policies that require complex multi-agent coordination is presented.
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