Dream to Adapt: Meta Reinforcement Learning by Latent Context
  Imagination and MDP Imagination
Lu Wen Songan Zhang H. Eric Tseng Huei Peng
Meta reinforcement learning (Meta RL) has been amply explored to quickly
learn an unseen task by transferring previously learned knowledge from similar
tasks. However, most state-of-the-art algorithms require the meta-training
tasks to have a dense coverage on the task distribution and a great amount of
data for each of them. In this paper, we propose MetaDreamer, a context-based
Meta RL algorithm that requires less real training tasks and data by doing
meta-imagination and MDP-imagination. We perform meta-imagination by
interpolating on the learned latent context space with disentangled properties,
as well as MDP-imagination through the generative world model where physical
knowledge is added to plain VAE networks. Our experiments with various
benchmarks show that MetaDreamer outperforms existing approaches in data
efficiency and interpolated generalization.