Efficient Parallel Reinforcement Learning Framework using the Reactor Model

Parallel Reinforcement Learning (RL) frameworks are essential for mapping RL workloads to multiple computational resources, allowing for faster generation of samples, estimation of values, and policy improvement. These computational paradigms require a seamless integration of training, serving, and simulation workloads. Existing frameworks, such as Ray, are not managing this orchestration efficiently, especially in RL tasks that demand intensive input/output and synchronization between actors on a single node. In this study, we have proposed a solution implementing the reactor model, which …

computational cs.dc cs.lg faster framework frameworks improvement integration mapping multiple orchestration policy ray reactor reinforcement reinforcement learning resources samples seamless integration simulation training values workloads