Relevance-Guided Modeling of Object Dynamics for Reinforcement Learning

Understand

Current deep reinforcement learning (RL) approaches incorporate minimal prior knowledge about the environment, limiting computational and sample efficiency.

• \textit{Objects} provide a succinct and causal description of the world, and many recent works have proposed unsupervised object representation learning using priors and losses over static object properties like visual consistency.
• However, object dynamics and interactions are also critical cues for objectness.
• In this paper we propose a framework for reasoning about object dynamics and behavior to rapidly determine minimal and task-specific object representations.