Learning Task-Driven Control Policies via Information Bottlenecks

Understand

This paper presents a reinforcement learning approach to synthesizing task-driven control policies for robotic systems equipped with rich sensory modalities (e.g., vision or depth).

• Standard reinforcement learning algorithms typically produce policies that tightly couple control actions to the entirety of the system's state and rich sensor observations.
• As a consequence, the resulting policies can often be sensitive to changes in task-irrelevant portions of the state or observations (e.g., changing background colors).
• In contrast, the approach we present here learns to create a task-driven representation that is used to compute control actions.