Learning Diverse Risk Preferences in Population-based Self-play

Understand

Among the great successes of Reinforcement Learning (RL), self-play algorithms play an essential role in solving competitive games.

• Current self-play algorithms optimize the agent to maximize expected win-rates against its current or historical copies, making it often stuck in the local optimum and its strategy style simple and homogeneous.
• A possible solution is to improve the diversity of policies, which helps the agent break the stalemate and enhances its robustness when facing different opponents.
• However, enhancing diversity in the self-play algorithms is not trivial.