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rank then act · corl-team

Rank-Then-Act research code for control from frame-order progress.

embodied-aipytorchreinforcement-learningrepresentation-learningrobot-learning
Publisher repository ↗
rank then act · corl-team resource page screenshot
Resource screenshot · captured 2026-10-08 · visually reviewed

Overview

Rank-Then-Act research code for control from frame-order progress.

Publisher summary: “Official implementation of the paper "Rank-Then-Act: Reward-Free Control from Frame-Order Progress"”

Use its repository to inspect the implementation, documentation and supported workflows. This directory record identifies the maintainer, code license and related resources; it does not claim that the project has been installed or tested on your robot.

Project information

Maintainer
corl-team
Primary language
Python
Repository code license
MIT
Model-weight license
Not recorded
Dataset license
Not recorded
Hardware design license
Not recorded
Maintenance
Not archived in the last recorded metadata
Last observed repository push
2026-09-15

Code licenses do not automatically cover model weights, datasets or hardware designs. Unrecorded license details require confirmation with the publisher.

Documentation sections

  • Rank-Then-Act
  • Reward-Free Control from Frame-Order Progress
  • Method Overview
  • Requirements
  • Repository structure
  • Setup

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