marl-jax: Multi-Agent Reinforcement Leaning Framework

Recent advances in Reinforcement Learning (RL) have led to many exciting applications. These advancements have been driven by improvements in both algorithms and engineering, which have resulted in faster training of RL agents. We present marl-jax, a multi-agent reinforcement learning software package for training and evaluating social generalization of the agents. The package is designed for training a population of agents in multi-agent environments and evaluating their ability to generalize to diverse background agents. It is built on top of DeepMind's JAX ecosystem~\cite{deepmind2020jax} and leverages the RL ecosystem developed by DeepMind. Our framework marl-jax is capable of working in cooperative and competitive, simultaneous-acting environments with multiple agents. The package offers an intuitive and user-friendly command-line interface for training a population and evaluating its generalization capabilities. In conclusion, marl-jax provides a valuable resource for researchers interested in exploring social generalization in the context of MARL. The open-source code for marl-jax is available at: \href{https://github.com/kinalmehta/marl-jax}{https://github.com/kinalmehta/marl-jax}Comment: Accepted at ECML-PKDD 2023 Demo Trac

Effects of Spectral Normalization in Multi-agent Reinforcement Learning

A reliable critic is central to on-policy actor-critic learning. But it becomes challenging to learn a reliable critic in a multi-agent sparse reward scenario due to two factors: 1) The joint action space grows exponentially with the number of agents 2) This, combined with the reward sparseness and environment noise, leads to large sample requirements for accurate learning. We show that regularising the critic with spectral normalization (SN) enables it to learn more robustly, even in multi-agent on-policy sparse reward scenarios. Our experiments show that the regularised critic is quickly able to learn from the sparse rewarding experience in the complex SMAC and RWARE domains. These findings highlight the importance of regularisation in the critic for stable learning

RoRD: Rotation-Robust Descriptors and Orthographic Views for Local Feature Matching

The use of local detectors and descriptors in typical computer vision pipelines works well until variations in viewpoint and appearance change become extreme. Past research in this area has typically focused on one of two approaches to this challenge: the use of projections into spaces more suitable for feature matching under extreme viewpoint changes, and attempting to learn features that are inherently more robust to viewpoint change. In this paper, we present a novel framework that combines the learning of invariant descriptors through data augmentation and orthographic viewpoint projection. We propose rotation-robust local descriptors, learnt through training data augmentation based on rotation homographies, and a correspondence ensemble technique that combines vanilla feature correspondences with those obtained through rotation-robust features. Using a range of benchmark datasets as well as contributing a new bespoke dataset for this research domain, we evaluate the effectiveness of the proposed approach on key tasks including pose estimation and visual place recognition. Our system outperforms a range of baseline and state-of-the-art techniques, including enabling higher levels of place recognition precision across opposing place viewpoints, and achieves practically useful performance levels even under extreme viewpoint changes. We reduce pose estimation error by 86.72% relative to state of the art. </p

COVID-19 Pathophysiology and Clinical Effects on Multiple Organ Systems - A Narrative Review

Patients with comorbidities including Hypertension (HTN), Diabetes Mellitus (DM), Chronic Obstructive Pulmonary Disease (COPD), Asthma, Obesity, Cardiovascular Disease (CVD), Chronic Kidney Disease (CKD), and those who are immunocompromised are prone to more severe complications of COVID-19 and a higher rate of hospitalizations. In the United States, around 94% of COVID-19 deaths had an average of 2.6 additional conditions or causes per death. In a summary report published by the Chinese Centre for Disease Control and Prevention of 72,314 cases, case-fatality rate was elevated among those with preexisting comorbid conditions—10.5% for cardiovascular disease, 7.3% for diabetes, 6.3% for chronic respiratory disease, 6.0% for HTN, and 5.6% for cancer. The COVID-19 pandemic continues to threaten people and healthcare systems globally and therefore the global economy. Currently, there is no cure or vaccine for COVID-19 and there is an urgent need to develop target therapies as we continue to learn more about this novel virus. Without therapeutic interventions, much of how we contain the viral spread is prevention through mitigation strategies (social distancing, face masks, supportive care). Early suspicion of COVID-19 symptoms with radiological and laboratory assessments may play a major role in preventing severity of the COVID-19. With this literature review we aim to provide review of pathophysiology of Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2) and its clinical effects on multiple organ systems

COVID-19 Pathophysiology and Clinical Effects on Multiple Organ Systems: A Review

Patients with comorbidities including Hypertension (HTN), Diabetes Mellitus (DM), Chronic Obstructive Pulmonary Disease (COPD), Asthma, Obesity, Cardiovascular Disease (CVD), Chronic Kidney Disease (CKD), and those who are immunocompromised are prone to more severe complications of COVID-19 and a higher rate of hospitalizations. In the United States, around 94% of COVID-19 deaths had an average of 2.6 additional conditions or causes per death. In a summary report published by the Chinese Centre for Disease Control and Prevention of 72,314 cases, case-fatality rate was elevated among those with preexisting comorbid conditions—10.5% for cardiovascular disease, 7.3% for diabetes, 6.3% for chronic respiratory disease, 6.0% for HTN, and 5.6% for cancer. The COVID-19 pandemic continues to threaten people and healthcare systems globally and therefore the global economy. Currently, there is no cure or vaccine for COVID-19 and there is an urgent need to develop target therapies as we continue to learn more about this novel virus. Without therapeutic interventions, much of how we contain the viral spread is prevention through mitigation strategies (social distancing, face masks, supportive care). Early suspicion of COVID-19 symptoms with radiological and laboratory assessments may play a major role in preventing severity of the COVID-19. With this literature review we aim to provide review of pathophysiology of Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2) and its clinical effects on multiple organ systems