Recently, reinforcement learning (RL) based approaches have attracted increasing attention for dynamic resource management asRLhelpsautomatically adapttoaspecific userworkload.

Reinforcement learning (RL) [1] studies the problem of learning in sequential decision-making problems where the dynamics of the environment is unknown, but can be learnt by performing actions andobserving their outcome inanonline fashion. Asample-efficient RLagent must trade off the explorationneeded to collect information about the environment, and theexploitation of the experience gathered so far to gain as much reward as possible.

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Deep reinforcement learning methods traditionally struggle withtaskswhere environment rewards are particularly sparse.

Gradient techniques are inherently slow, sacrificing the "few-shot" quality

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The transition dynamics simply mixes an action and a random sampled latent. It then applies an exponential moving average for temporal persistency, the resulting latent is decoded to image using pretrained generator.