What is self-play in RL?

Self-play in reinforcement learning (RL) is a training method where an agent improves its skills by repeatedly competing against versions of itself. Instead of learning from a fixed environment or human-designed opponents, the agent generates its own training partners. Over time, the agent faces increasingly skilled opponents as it iteratively updates its policy, creating a feedback loop that drives improvement. This approach is particularly effective in competitive or adversarial scenarios, such as games, where the agent must adapt to diverse strategies.

A common implementation involves maintaining a pool of past agent versions. For example, in AlphaGo Zero, the AI played millions of games against earlier iterations of itself, using these matches to refine its neural network through trial and error. The agent starts with random actions but gradually discovers sophisticated strategies as it encounters stronger opponents. This mimics a natural learning progression: early opponents provide basic challenges, while later ones force the agent to handle complex tactics. In multi-agent environments like robotics simulations, self-play can help agents learn robust behaviors by exposing them to varied scenarios, such as competing goals or dynamic obstacles.

However, self-play has challenges. If not managed carefully, agents can develop over-specialized strategies that work only against specific opponents but fail in general settings. To avoid this, techniques like population-based training are used, where multiple agents with diverse strategies are trained simultaneously. For instance, OpenAI’s Dota 2 bots employed a “league” of agents, each specializing in different playstyles, ensuring adaptability. Additionally, balancing exploration (trying new strategies) and exploitation (using known effective tactics) is critical. Developers often combine self-play with domain randomization—varying environment parameters like physics or opponent strengths—to enhance generalization. While computationally intensive, self-play remains a powerful tool for training agents in complex, competitive domains without relying on pre-existing expert data.