Temporal Predictive Coding For Model-Based Planning In Latent Space
This addresses the problem of handling nuisance information in real-world environments for reinforcement learning practitioners, though it is incremental as it builds on existing representation learning approaches.
The paper tackles the challenge of high-dimensional observations in model-based reinforcement learning by using temporal predictive coding to encode temporally-predictable, task-relevant information into a latent space, showing superiority over existing methods in complex-background settings and competitiveness in standard settings.
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.