这篇论文提出用模型驱动和遗传算法自动生成RL环境变体，省去手写大量相似环境的麻烦，野火场景验证过，做课程学习的可以看看。
论文提出基于模型驱动的方法，通过混合遗传算法（结合全局搜索和启发式局部搜索）自动生成多组相似但不同的强化学习训练环境。方法将变异和约束表达为模型变换，由先进模型变换引擎操作化搜索过程。在野火缓解场景和课程学习（依赖环境家族的学习范式）中验证了方法的有效性。原型工具将手工开发环境家族的错误率降低，提升了可扩展性。
A Model-Driven Approach for Developing Families of Reinforcement Learning Environments
Virtual training environments are software-intensive systems in which reinforcement learning (RL) agents learn, adapt, and demonstrate meaningful behavior. Virtual training environments offer a safe and cost-efficient alternative to training agents in real-world settings. However, to converge, most realistic RL problems require training in multiple, mostly similar but slightly different environments - i.e., families of environment variants. The typical development process of environment families is a labor-intensive and error-prone manual endeavor that does not scale well. To alleviate these issues, in this paper, we propose a model-driven approach for developing families of RL training environments. To obtain the family of environments, we develop an approach and prototype tool. In our approach, a hybrid genetic algorithm - a combination of population-based global search and heuristic local search - generates environment families. Mutations and constraints are expressed as model transformations and are operationalized into a search process by a state-of-the-art model transformation engine. We demonstrate the soundness of our approach in a wildfire mitigation scenario and curriculum learning - a particular learning paradigm that relies on environment families.