Description: Variants of Q-Learning are adaptations or modifications of the standard Q-Learning algorithm, a fundamental method in the field of reinforcement learning. This algorithm is based on the idea that an agent can learn to make optimal decisions through interaction with an environment, updating its knowledge about actions and their respective rewards. Variants arise to address limitations of classical Q-Learning, such as slow convergence or inability to handle large and complex state spaces. Some of these variants include Dueling Q-Learning, which separates the estimation of state value and action advantage, and Double Q-Learning, which uses two sets of Q-values to reduce reward overestimation. Other adaptations, such as Prioritized Experience Replay, improve learning efficiency by prioritizing more relevant experiences. These variants allow researchers and developers to optimize algorithm performance in various applications, from gaming to robotics and recommendation systems, making Q-Learning a versatile and powerful tool in the broader field of machine learning.
History: null
Uses: null
Examples: null
