PEARL: Enabling Portable, Productive, and High-Performance Deep Reinforcement Learning using Heterogeneous Platforms

Deep Reinforcement Learning (DRL) is vital in various AI applications. DRL algorithms comprise diverse compute kernels, which may not be simultaneously optimized using a homogeneous architecture. However, even with available heterogeneous architectures, optimizing DRL performance remains a challenge due to the complexity of hardware and programming models employed in modern data centers. To address this, we introduce PEARL, a toolkit for composing parallel DRL systems on heterogeneous platforms consisting of general-purpose processors (CPUs) and accelerators (GPUs, FPGAs). Our innovations include: 1. A general training protocol agnostic of the underlying hardware, enabling portable implementations across various platforms. 2. Incorporation of DRL-specific optimizations on runtime scheduling and resource allocation, facilitating parallelized training and enhancing the overall system performance. 3. Automatic optimization of DRL task-to-device assignments through throughput estimation. 4. High-level API for productive development using the toolkit. We showcase our toolkit through experimentation with two widely used DRL algorithms, DQN and DDPG, on two diverse heterogeneous platforms. The generated implementations outperform state-of-the-art libraries for CPU-GPU platforms by up to 2.2× throughput improvements, and 2.4× higher performance portability across platforms.