Hyperparameter Tuning for Deep Reinforcement Learning Applications. (arXiv:2201.11182v1 [cs.LG])

Web: http://arxiv.org/abs/2201.11182

Reinforcement learning (RL) applications, where an agent can simply learn
optimal behaviors by interacting with the environment, are quickly gaining
tremendous success in a wide variety of applications from controlling simple
pendulums to complex data centers. However, setting the right hyperparameters
can have a huge impact on the deployed solution performance and reliability in
the inference models, produced via RL, used for decision-making. Hyperparameter
search itself is a laborious process that requires many iterations and
computationally expensive to find the …

applications arxiv deep learning reinforcement learning