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  • CatBoost (A fast, scalable, high performance Gradient Boosting on Decision Trees library, used for ranking, classification, regression and other machine learning tasks for Python, R, Java, C++. Supports computation on CPU and GPU)
  • XGBoost
  • FastAI (The fastai deep learning library, plus lessons and tutorials)
  • Xlearn (High performance, easy-to-use, and scalable machine learning (ML) package, including linear model (LR), factorization machines (FM), and field-aware factorization machines (FFM) for Python and CLI interface)

These algorithms help address the Markov violation from a dynamic environment meaning algorithms like DQN can not converge. The policy networks have dependencies on each other.