In recent years, a proliferation of methods were developed for cooperative multi-agent reinforcement learning (c-MARL). However, the robustness of c-MARL agents against adversarial attacks has been rarely explored. In this paper, we propose to evaluate the robustness of c-MARL agents via a model-based approach, named c-MBA. Our proposed formulation can craft much stronger adversarial state perturbations of c-MARL agents to lower total team rewards than existing model-free approaches. In addition, we propose the first victim-agent selection strategy and the first data-driven approach to define targeted failure states where each of them allows us to develop even stronger adversarial attack without the expert knowledge to the underlying environment. Our numerical experiments on two representative MARL benchmarks illustrate the advantage of our approach over other baselines: our model-based attack consistently outperforms other baselines in all tested environments.

Learning never stops for successful workers, who must grow their careers while coping with the changing expectations of employers. Robust job-skill representations can empower workers by helping them to better decipher viable job changes given their current skill set and guide them toward skills they can learn to meet career goals. In this work we combine threads of research in economics and AI to improve upon existing job-skill representation methodology and performance. We build a benchmark dataset of between-job transitions from US Census data and show that a representation trained on a large set of online job postings via a transformer-based architecture outperforms existing baselines. Further analysis demonstrates that this model is better able to transfer across taxonomies than existing models.

Today’s fast changing workplace necessitates constant reskilling of the workforce at both the corporate and national level. Current approaches to reskilling depend on manual logic, which can be time-consuming and expensive due to their dependence on manual labour. In this paper, we propose a scalable machine-learning driven alternative by introducing a method to make reskilling recommendations using word embeddings of skill keywords trained on a corpus of historical job listings and resumes. We achieve this by training dense vector embeddings to represent skill keywords using Word2Vec and fine-tuned BERT models, allowing us to make comparisons between skills. Given an individual’s current skills, this model is leveraged to identify which skills to prioritize for their development based on their target role and to recommend reskilling plans based on the identified skill gap. The proposed framework has the potential to aid both public and private organizations to better direct their educational resources to individuals.