  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 and
meta learning, as well as the robust and adversarial reinforcement learning.
However, there is not a unified formulation of the various schemes, as well as
the comprehensive comparisons of methods across different schemes. In this
work, we propose a game-theoretic framework for the generalization in
reinforcement learning, named GiRL, where an RL agent is trained against an
adversary over a set of tasks, where the adversary can manipulate the
distributions over tasks within a given threshold. With different
configurations, GiRL can reduce the various schemes mentioned above. To solve
GiRL, we adapt the widely-used method in game theory, policy space response
oracle (PSRO) with the following three important modifications: i) we use
model-agnostic meta learning (MAML) as the best-response oracle, ii) we propose
a modified projected replicated dynamics, i.e., R-PRD, which ensures the
computed meta-strategy of the adversary fall in the threshold, and iii) we also
propose a protocol for the few-shot learning of the multiple strategies during
testing. Extensive experiments on MuJoCo environments demonstrate that our
proposed methods can outperform existing baselines, e.g., MAML.
