Log In Sign Up

Temporal Predictive Coding For Model-Based Planning In Latent Space

by   Tung Nguyen, et al.

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.


page 7

page 17


Predictive Coding for Locally-Linear Control

High-dimensional observations and unknown dynamics are major challenges ...

Learning Latent State Spaces for Planning through Reward Prediction

Model-based reinforcement learning methods typically learn models for hi...

Prototypical context-aware dynamics generalization for high-dimensional model-based reinforcement learning

The latent world model provides a promising way to learn policies in a c...

Learning Invariant Representations for Reinforcement Learning without Reconstruction

We study how representation learning can accelerate reinforcement learni...

Comparing Reconstruction- and Contrastive-based Models for Visual Task Planning

Learning state representations enables robotic planning directly from ra...

Robust Robotic Control from Pixels using Contrastive Recurrent State-Space Models

Modeling the world can benefit robot learning by providing a rich traini...

Mutual Information Maximization for Robust Plannable Representations

Extending the capabilities of robotics to real-world complex, unstructur...