This paper delves into the adaptability of Proximal Policy Optimization (PPO)-trained agents within dynamic environments. Typically, an agent is trained within a specific environment, learning to maximise reward acquisition and to navigate it effectively. However, alterations to this environment can lead to performance deficiencies. Existing research does not fully elucidate how the training of agents influences their adaptability in different environments and which parameters significantly impact this. This study aims to fill this gap, contributing to the creation of more versatile intelligent agents. The objective of this study is to explore how training agents in various environments affects their adaptability when introduced to unfamiliar environments. To this end, 36 models were trained using 36 different configurations to play a one-versus-one (1v1) Snake game. These models were subsequently compared against each configuration to measure their adaptability. The results reveal that map size substantially affect the adaptability of agents in different environments. Interestingly, the results showed that the most adaptive agents were not those trained on the most expansive and complex environment, but rather the simplest.

The paper describes results of an empirical study, where some hypotheses about the impact of reuse on defect-density and stability, and about the impact of component size on defects and defect-density in the context of reuse are assessed, using historical data (data mining) on defects, modification rate, and software size of a large-scale telecom system developed by Ericsson. The analysis showed that reused components have lower defect-density than non-reused ones. Reused components have more defects with highest severity than the total distribution, but less defects after delivery, which shows that that these are given higher priority to fix. There are an increasing number of defects with component size for non-reused components, but not for reused components. Reused components were less modified (more stable) than non-reused ones between successive releases, even if reused components must incorporate evolving requirements from several application products. The study furthermore revealed inconsistencies and weaknesses in the existing defect reporting system, by analyzing data that was hardly treated systematically before.