A Heterogeneous Acceleration System for Attention-Based Multi-Agent Reinforcement Learning

Multi-Agent Reinforcement Learning (MARL) is an emerging technology that has seen success in many AI applications. Multi-Actor-Attention-Critic (MAAC) is a state-of-the-art MARL algorithm that uses a Multi-Head Attention (MHA) mechanism to learn messages communicated among agents during the training process. Current implementations of MAAC using CPU and CPU-GPU platforms lack fine-grained parallelism among agents, sequentially executing each stage of the training loop, and their performance suffers from costly data movement involved in MHA communication learning. In this work, we develop the first high-throughput accelerator for MARL with attention-based communication on a CPU-FPGA heterogeneous system. We alleviate the limitations of existing implementations through a combination of data- and pipeline-parallel modules in our accelerator design and enable fine-grained system scheduling for exploiting concurrency among heterogeneous resources. Our design increases the overall system throughput by 4.6x and 4.1x compared to CPU and CPU-GPU implementations, respectively.