  We are interested in learning models of non-stationary environments, which
can be framed as a multi-task learning problem. Model-free reinforcement
learning algorithms can achieve good asymptotic performance in multi-task
learning at a cost of extensive sampling, due to their approach, which requires
learning from scratch. While model-based approaches are among the most data
efficient learning algorithms, they still struggle with complex tasks and model
uncertainties. Meta-reinforcement learning addresses the efficiency and
generalization challenges on multi task learning by quickly leveraging the
meta-prior policy for a new task. In this paper, we propose a
meta-reinforcement learning approach to learn the dynamic model of a
non-stationary environment to be used for meta-policy optimization later. Due
to the sample efficiency of model-based learning methods, we are able to
simultaneously train both the meta-model of the non-stationary environment and
the meta-policy until dynamic model convergence. Then, the meta-learned dynamic
model of the environment will generate simulated data for meta-policy
optimization. Our experiment demonstrates that our proposed method can
meta-learn the policy in a non-stationary environment with the data efficiency
of model-based learning approaches while achieving the high asymptotic
performance of model-free meta-reinforcement learning.
