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Survey Results for Now
Survey Results for Now
The survey shows:
Despite the fast development of multi-agent reinforcement learning (MARL) algorithms, there is a lack of commonly-acknowledged baseline implementation and evaluation benchmarks.
An urgent need for MARL researchers is to develop a unified benchmark suite, similar to the role of RLlib in single-agent RL, that can support both high-performance MARL implementations and replicable evaluations in various testing environments.
Multi-Agent RLlib is a flexible and easy-to-use tool for MARL research.
Multi-Agent RLlib is a flexible and easy-to-use tool for MARL research.
It designed to meet the needs of researchers.
It designed to meet the needs of researchers.
You are so welcomed to use and contribute to the project.
You are so welcomed to use and contribute to the project.
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