Generalization in reinforcement learning (RL) is of importance for real deployment of RL algorithms. Various schemes are proposed to address the generalization issues, including transfer learning, multi-task learning, meta learning, as well as robust and adversarial reinforcement learning. However, there is not a unified formulation of various schemes and comprehensive comparisons of methods across different schemes. In this work, we propound GiRL, a game-theoretic framework for generalization i…