The study proposes a structural categorization of MARL algorithms based on their computational characteristics and reveals the importance of communication in coordinating agent behaviors. It also compares various MARL algorithms, noting the trade-offs between different categories in terms of communication methods and training schemes. The research concludes by underscoring the importance of considering latency-bounded throughput as a key metric in future MARL research and suggests exploring specialized accelerator designs to reduce communication overheads and employ fine-grained task mapping using heterogeneous platforms.
Key takeaways:
- The study emphasizes the importance of latency-bounded throughput in Multi-Agent Reinforcement Learning (MARL) implementations and introduces a comprehensive taxonomy of MARL algorithms based on training scheme and communication method.
- MARL training can be time-intensive, often spanning days to months, due to the need for inter-agent communications, which poses unique challenges.
- A structural categorization of MARL algorithms was proposed based on their computational characteristics, revealing that communication, especially in a decentralized setting, is vital in coordinating agent behaviors.
- The research suggests the need for specialized accelerator designs to reduce communication overheads and employ fine-grained task mapping using heterogeneous platforms in future MARL research.