Explore an in-depth author interview on ACCEL: Evolving Curricula with Regret-Based Environment Design. Delve into the innovative approach of combining adversarial adaptiveness of regret-based sampling methods with level-editing capabilities for creating curricula in reinforcement learning. Gain insights on minimax regret, level selection, domain-specific knowledge requirements, and the emergence of generalization in AI agents. Discover the potential applications, challenges, and future directions of this cutting-edge research in automatic curriculum generation for multi-capable agents.