Learning Actionable Representations with Goal-Conditioned Policies

Understand

Representation learning is a central challenge across a range of machine learning areas.

• In reinforcement learning, effective and functional representations have the potential to tremendously accelerate learning progress and solve more challenging problems.
• Most prior work on representation learning has focused on generative approaches, learning representations that capture all underlying factors of variation in the observation space in a more disentangled or well-ordered manner.
• In this paper, we instead aim to learn functionally salient representations: representations that are not necessarily complete in terms of capturing all factors of variation in the observation space, but rather aim to capture those factors of variation that are important for decision making -- that are "actionable." These representations are aware of the dynamics of the environment, and capture only the elements of the observation that are necessary for decision making rather than all factors of variation, without explicit reconstruction of the observation.