  High-dimensional observations are a major challenge in the application of
model-based reinforcement learning (MBRL) to real-world environments. To handle
high-dimensional sensory inputs, existing approaches use representation
learning to map high-dimensional observations into a lower-dimensional latent
space that is more amenable to dynamics estimation and planning. In this work,
we present an information-theoretic approach that employs temporal predictive
coding to encode elements in the environment that can be predicted across time.
Since this approach focuses on encoding temporally-predictable information, we
implicitly prioritize the encoding of task-relevant components over nuisance
information within the environment that are provably task-irrelevant. By
learning this representation in conjunction with a recurrent state space model,
we can then perform planning in latent space. We evaluate our model on a
challenging modification of standard DMControl tasks where the background is
replaced with natural videos that contain complex but irrelevant information to
the planning task. Our experiments show that our model is superior to existing
methods in the challenging complex-background setting while remaining
competitive with current state-of-the-art models in the standard setting.
