IEEE

Meta Learning Task Representation in Multiagent Reinforcement Learning: From Global Inference to Local Inference

Abstract: Multiagent meta reinforcement learning (MAMRL) enables multiagent systems (MASs) to adapt to multiple tasks. However, partial observability poses a significant challenge by hindering ...
EurekAlert!

Explanation-based retrieval boosts grammatical error correction

Grammatical error correction (GEC) is a key task in natural language processing (NLP), widely applied in education, news, and ...
Yale Insights

Most Startups Fail. These Founders Thought Making an Impact Was Worth the Risk.

We talked with Nick Callegari ’25, Dianna Liu ’18, and Ariana Yuen ’19 about the unique challenges faced by founders trying to make a difference and the moments that make their work worthwhile.
IEEE

Optimized Task Offloading in Multi-Domain IoT Networks Using Distributed Deep Reinforcement Learning in Edge Computing Environments

Abstract: In the Internet of Things (IoT) networks, sensors, gateways, and services interoperate at different levels to provide services to the end users. IoT networks are deployed in different ...