  Reinforcement Learning (RL) trains agents to learn optimal behavior by
maximizing reward signals from experience datasets. However, RL training often
faces memory limitations, leading to execution latencies and prolonged training
times. To overcome this, SwiftRL explores Processing-In-Memory (PIM)
architectures to accelerate RL workloads. We achieve near-linear performance
scaling by implementing RL algorithms like Tabular Q-learning and SARSA on
UPMEM PIM systems and optimizing for hardware. Our experiments on OpenAI GYM
environments using UPMEM hardware demonstrate superior performance compared to
CPU and GPU implementations.
