At run time, a group of agents interact with each other in a shared environment.

In this paper, we identify and analyze a recurring training loss pattern, which we term the \textit{Epochal Sawtooth Effect (ESE)}, commonly observed during training with adaptive gradient-based optimizers, particularly Adam optimizer. This pattern is characterized by a sharp drop in loss at the beginning of each epoch, followed by a gradual increase, resulting in a sawtooth-shaped loss curve. Through empirical observations, we demonstrate that while this effect is most pronounced with Adam, it persists, although less severely, with other optimizers such as RMSProp. We provide an in-depth explanation of the underlying mechanisms that lead to the Epochal Sawtooth Effect. The influences of factors like \(\beta\), batch size, data shuffling on this pattern have been studied. We quantify the influence of \(\beta_2\) on the shape of the loss curve, showing that higher values of \(\beta_2\) result in a nearly linear increase in loss, while lower values create a concave upward trend. Our analysis reveals that this behavior stems from the adaptive learning rate controlled by the second moment estimate, with \(\beta_1\) playing a minimal role when \(\beta_2\) is large. To support our analysis, we replicate this phenomenon through a controlled quadratic minimization task. By incrementally solving a series of quadratic optimization problems using Adam, we demonstrate that the Epochal Sawtooth Effect can emerge even in simple optimization scenarios, reinforcing the generality of this pattern. This paper provides both theoretical insights and quantitative analysis, offering a comprehensive understanding of this ubiquitous phenomenon in modern optimization techniques.

In a new study conducted by Siamak Yousefi and colleagues, it was found that an automated machine learning method can detect patterns of visual field (VF) loss and provide objective, reproducible terminology for describing early indicators of visual abnormalities and rapid progression in glaucoma patients. The findings of this study were published in Ophthalmology. This was a cross-sectional and longitudinal study that followed 2231 aberrant VFs from 205 eyes of 176 OHTS individuals for almost 16 years. An unsupervised deep archetypal analysis method and an OHTS certified VF reader were used to discover common patterns of VF loss. Machine-identified glaucoma damage patterns were compared to those previously described (expert-identified) in the OHTS in 2003.

Recent studies have shown that introducing communication between agents can significantly improve overall performance in cooperative Multi-agent reinforcement learning (MARL). However, existing communication schemes often require agents to exchange an excessive number of messages at run-time under a reliable communication channel, which hinders its practicality in many real-world situations. In this paper, we present \textit{Temporal Message Control} (TMC), a simple yet effective approach for achieving succinct and robust communication in MARL. TMC applies a temporal smoothing technique to drastically reduce the amount of information exchanged between agents. Experiments show that TMC can significantly reduce inter-agent communication overhead without impacting accuracy. Furthermore, TMC demonstrates much better robustness against transmission loss than existing approaches in lossy networking environments.