# Self-play reinforcement learning

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%.

Researchers from MIT, Carnegie Mellon University, New York University, and Stanford University developed an AI system called Ataraxos that combines a self-play reinforcement learning "blueprint strategy" with decision-time planning using a generative model, defeating the strongest human Stratego player by a record 15-1-4 margin and achieving a 39-2 record against top players at the Stratego world championship, while using less than one hundredth of DeepMind's DeepNash training examples and less than one thirtieth of its self-play games, and generalizing to other imperfect-information games such as Barrage Stratego, Hanabi, and Dou dizhu.

Researchers from MIT, Carnegie Mellon University, New York University, and Stanford University developed an AI system called Ataraxos that combines a self-play reinforcement learning "blueprint strategy" with decision-time planning using a generative model, defeating the strongest human Stratego player by a record 15-1-4 margin and achieving a 39-2 record against top players at the Stratego world championship, while using less than one hundredth of DeepMind's DeepNash training examples and less than one thirtieth of its self-play games, and generalizing to other imperfect-information games such as Barrage Stratego, Hanabi, and Dou dizhu.