  Model-based reinforcement learning (RL) enjoys several benefits, such as
data-efficiency and planning, by learning a model of the environment's
dynamics. However, learning a global model that can generalize across different
dynamics is a challenging task. To tackle this problem, we decompose the task
of learning a global dynamics model into two stages: (a) learning a context
latent vector that captures the local dynamics, then (b) predicting the next
state conditioned on it. In order to encode dynamics-specific information into
the context latent vector, we introduce a novel loss function that encourages
the context latent vector to be useful for predicting both forward and backward
dynamics. The proposed method achieves superior generalization ability across
various simulated robotics and control tasks, compared to existing RL schemes.
