Characterizing Speed Performance of Multi-Agent Reinforcement Learning

• URL: http://arxiv.org/abs/2309.07108v1
• Date: Wed, 13 Sep 2023 17:26:36 GMT
• Title: Characterizing Speed Performance of Multi-Agent Reinforcement Learning
• Authors: Samuel Wiggins, Yuan Meng, Rajgopal Kannan, Viktor Prasanna
• Abstract summary: Multi-Agent Reinforcement Learning (MARL) has achieved significant success in large-scale AI systems and big-data applications such as smart grids, surveillance, etc. Existing advancements in MARL algorithms focus on improving the rewards obtained by introducing various mechanisms for inter-agent cooperation. We analyze the speed performance (i.e., latency-bounded throughput) as the key metric in MARL implementations.
• Score: 5.313762764969945
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Multi-Agent Reinforcement Learning (MARL) has achieved significant success in large-scale AI systems and big-data applications such as smart grids, surveillance, etc. Existing advancements in MARL algorithms focus on improving the rewards obtained by introducing various mechanisms for inter-agent cooperation. However, these optimizations are usually compute- and memory-intensive, thus leading to suboptimal speed performance in end-to-end training time. In this work, we analyze the speed performance (i.e., latency-bounded throughput) as the key metric in MARL implementations. Specifically, we first introduce a taxonomy of MARL algorithms from an acceleration perspective categorized by (1) training scheme and (2) communication method. Using our taxonomy, we identify three state-of-the-art MARL algorithms - Multi-Agent Deep Deterministic Policy Gradient (MADDPG), Target-oriented Multi-agent Communication and Cooperation (ToM2C), and Networked Multi-Agent RL (NeurComm) - as target benchmark algorithms, and provide a systematic analysis of their performance bottlenecks on a homogeneous multi-core CPU platform. We justify the need for MARL latency-bounded throughput to be a key performance metric in future literature while also addressing opportunities for parallelization and acceleration.

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