#700#Research_Paper#710#methodology#710.00#Multi-agent_Reinforcement_Learning#710.00 a#Base_Domains

테스트용한글

Methodology

Multi-agent Reinforcement Learning

359 papers with code • 3 benchmarks • 8 datasets

The target of Multi-agent Reinforcement Learning is to solve complex problems by integrating multiple agents that focus on different sub-tasks. In general, there are two types of multi-agent systems: independent and cooperative systems.

Source: Show, Describe and Conclude: On Exploiting the Structure Information of Chest X-Ray Reports

Domain Generalization

567 papers with code • 19 benchmarks • 24 datasets

The idea of Domain Generalization is to learn from one or multiple training domains, to extract a domain-agnostic model which can be applied to an unseen domain

Source: Diagram Image Retrieval using Sketch-Based Deep Learning and Transfer Learning