Concerning the problem that a straightforward implementation of the experience-replay procedure based on python data-structures may lead to a performance bottleneck in Deep Q Network (DQN) related applications, a design scheme of a universal experience-replay module was proposed to provide high performance. The proposed module consists of two software layers. One of them, called the "kernel", was written in C++, to implement fundamental functions for experience-replay, achieving a high execution efficiency. And the other layer "wrapper", written in python, encapsulated the module function and provided the call interface in an object-oriented style, guaranteeing the usability. For the critical operations in experience-replay, the software structure and algorithms were well researched and designed. The measures include implementing the priority replay mechanism as an accessorial part of the main module with logical separation, bringing forward the samples' verification of "get_batch" to the "record" operation, using efficient strategies and algorithms in eliminating samples, and so on. With such measures, the proposed module is universal and extendible. The experimental results show that the execution efficiency of the experience-replay process is well optimized by using the proposed module, and the two critical operations, the "record" and the "get_batch", can be executed efficiently. The proposed module operates the "get_batch" about 100 times faster compared with the straightforward implementation based on python data-structures. Therefore, the experience-replay process is no longer a performance bottleneck in the system, meeting the requirements of various kinds of DQN-related applications.

According to the problem of the existing garbage collection algorithm for NAND flash memory, an efficient algorithm, called AWGC (Age With Garbage Collection), was presented to improve wear leveling of NAND flash memory. A hybrid policy with the age of invalid page, erase count of physical blocks and the update frequency of physical blocks were redefined to select the returnable block. Meanwhile, a new heat calculation method logic pages was deduced, and cold-hot separating of valid pages in returnable block was conducted. Compared with the GReedy (GR) algorithm, Cost-Benefit (CB) algorithm, Cost-Age-Time (CAT) algorithm and File-aware Garbage Collection (FaGC) algorithm, not only some good results in wear leveling have been got, but also the total numbers of erase and copy operations have significantly been reduced.