**Experiential Reinforcement Learning (ERL)** is a novel training paradigm that enhances how AI agents learn by incorporating a structured **experience-reflection-consolidation loop**. Unlike standard reinforcement learning, which often relies on trial-and-error driven by simple numerical rewards, ERL requires agents to **verbally reflect** on their failures and environment feedback to improve subsequent attempts. These successful corrections are then **internalized** into the base model through distillation, allowing the agent to perform better in the future without needing to reflect during actual deployment. Across diverse tasks like **Sokoban** and **HotpotQA**, this method significantly boosts **learning efficiency** and final performance by transforming raw interaction data into actionable reasoning. By using a **cross-episode memory** to store effective strategies, ERL shifts the focus of machine learning from implicit optimization toward **explicit behavioral revision**. These findings suggest that grounding reinforcement learning in deliberate self-reflection creates more robust and adaptable agentic systems.