  The generalization ability of most meta-reinforcement learning (meta-RL)
methods is largely limited to test tasks that are sampled from the same
distribution used to sample training tasks. To overcome the limitation, we
propose Latent Dynamics Mixture (LDM) that trains a reinforcement learning
agent with imaginary tasks generated from mixtures of learned latent dynamics.
By training a policy on mixture tasks along with original training tasks, LDM
allows the agent to prepare for unseen test tasks during training and prevents
the agent from overfitting the training tasks. LDM significantly outperforms
standard meta-RL methods in test returns on the gridworld navigation and MuJoCo
tasks where we strictly separate the training task distribution and the test
task distribution.
