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Optimizing Reinforcement Learning Libraries
This chapter focuses on enhancing the performance of reinforcement learning (RL) models, addressing CPU overhead from training and evaluation mode switches. It introduces TensorDict, a versatile data structure aimed at streamlining diverse data handling while exploring its broad applications in AI. Additionally, the discussion emphasizes the importance of effective library design in catering to both researcher needs and innovative use cases.