FLASH TRANSLATION LAYER DESIGN USING REINFORCEMENT LEARNING

The subject matter described herein provides systems and techniques to counter a high write amplification in physical memory, to ensure the longevity of the physical memory, and to ensure that the physical memory wears in a more uniform manner. In this regard, aspects of this disclosure include the design of a Flash Translation Layer (FTL), which may manage logical to physical mapping of data within the physical memory. In particular, the FTL may be designed with a mapping algorithm, which uses reinforcement learning (RL) to optimize data mapping within the physical memory. The RL technique may use a Bellman equation with q-learning that may rely on a table being updated with entries that take into account at least one of a state, an action, a reward, or a policy. The RL technique may also make use a deep neural network to predict particular values of the table.