Planning to Practice: Efficient Online Fine-Tuning by Composing Goals in Latent Space

Understand

General-purpose robots require diverse repertoires of behaviors to complete challenging tasks in real-world unstructured environments.

• To address this issue, goal-conditioned reinforcement learning aims to acquire policies that can reach configurable goals for a wide range of tasks on command.
• However, such goal-conditioned policies are notoriously difficult and time-consuming to train from scratch.
• In this paper, we propose Planning to Practice (PTP), a method that makes it practical to train goal-conditioned policies for long-horizon tasks that require multiple distinct types of interactions to solve.