Experience Driven Reinforcement Learning

Experience-driven reinforcement learning (EDRL) focuses on leveraging past experiences to improve the efficiency and adaptability of reinforcement learning agents. Current research explores EDRL's application in diverse areas, including procedural content generation (e.g., game levels tailored to player affect), robotics (e.g., quadrupedal locomotion planning), and the development of self-organizing neural networks with structural plasticity. These advancements aim to create more robust and efficient AI systems capable of adapting to dynamic environments and complex tasks, impacting fields ranging from entertainment to autonomous systems.