Reinforcement learning has deep roots, tracing back to control theory and inventory management. Significant milestones include the first actor-critic algorithm in 1983 and the introduction of Q learning in 1989. These foundational concepts paved the way for modern advancements, demonstrating that the field is far from new, despite its recent popularity.
