Exploration Exploitation

The exploration-exploitation dilemma describes the fundamental challenge in decision-making systems of balancing the need to explore unknown options against the desire to exploit known, rewarding ones. Current research focuses on improving the efficiency of this trade-off across various domains, employing techniques like contextual bandits, Thompson sampling, and Bayesian optimization, often integrated with neural networks and graph neural networks to handle complex data and large action spaces. These advancements are significantly impacting fields like reinforcement learning, recommendation systems, and automated planning, leading to more efficient algorithms and improved performance in diverse applications.