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Distributed Reinforcement Learning (RL) frameworks are essential for mappingRL workloads to multiple computational resources, allowing for fastergeneration of samples, estimation of values, and policy improvement. Thesecomputational paradigms require a seamless integration of training, serving,and simulation workloads. Existing frameworks, such as Ray, are not managingthis orchestration efficiently. In this study, we've proposed a solutionimplementing Reactor Model, which enforces a set of actors to have a fixedcommunication pattern. This allows the scheduler to eliminate works needed forsynchronization, such as acquiring and releasing locks for each actor orsending and processing coordination-related messages. Our framework, LinguaFranca (LF), a coordination language based on the Reactor Model, also providesa unified interface that allows users to automatically generate dataflow graphsfor distributed RL. On average, LF outperformed Ray in generating samples fromOpenAI Gym and Atari environments by 1.21x and 11.62x, reduced the averagetraining time of synchronized parallel Q-learning by 31.2%, and acceleratedMulti-Agent RL inference by 5.12x.

Optimizing Distributed Reinforcement Learning with Reactor Model and Lingua Franca