A hybrid genetic algorithm with model transformations generates families of RL training environments, demonstrated for wildfire mitigation and curriculum learning.
Model Driven Architecture - Foundations and Applications: 5th European Conference, ECMDA-FA 2009, Enschede, The Netherlands, June 23-26, 2009 , publisher =
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
fields
cs.SE 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
ProMoTA integrates process modeling with automated end-to-end traceability generation and analysis for model transformation chains in MDE, demonstrated on a wireless sensor network IoT application.
citing papers explorer
-
A Model-Driven Approach for Developing Families of Reinforcement Learning Environments
A hybrid genetic algorithm with model transformations generates families of RL training environments, demonstrated for wildfire mitigation and curriculum learning.
-
ProMoTA: a model-driven framework for end-to-end traceability analysis
ProMoTA integrates process modeling with automated end-to-end traceability generation and analysis for model transformation chains in MDE, demonstrated on a wireless sensor network IoT application.