# Game-Guided Skill Discovery

The work presents Game-Guided Skill Discovery (GGSD), in which a hierarchical agent self-plays 1v1 competitive games against a pool of past checkpoints, a high-level policy selects among 5 discrete skills (6 for G1) and a skill-conditioned low-level policy outputs motor actions, with a mutual-information reward separating skill semantics; after training a human can replace the high-level policy and use the same discrete skills, composing them on Ant, a Franka arm and a Unitree G1 to solve unseen Maze and CubePush tasks, with human success rates of at least 84%.

The work presents Game-Guided Skill Discovery (GGSD), in which a hierarchical agent self-plays 1v1 competitive games against a pool of past checkpoints, a high-level policy selects among 5 discrete skills (6 for G1) and a skill-conditioned low-level policy outputs motor actions, with a mutual-information reward separating skill semantics; after training a human can replace the high-level policy and use the same discrete skills, composing them on Ant, a Franka arm and a Unitree G1 to solve unseen Maze and CubePush tasks, with human success rates of at least 84%.