Description: Dual Q-Learning is an extension of Q-learning that maintains two separate Q-value estimates. This technique is used in the field of reinforcement learning, where an agent learns to make optimal decisions through interaction with an environment. Unlike traditional Q-learning, which uses a single Q-value table to represent the quality of actions in each state, Dual Q-Learning introduces two tables: one for the actions that are chosen and another for the actions that are not chosen. This separation allows the agent to have a better representation of uncertainty and variability in value estimates, which can lead to faster convergence and better exploration of the action space. Additionally, using two estimates helps mitigate the problem of overestimation of Q-values, a common phenomenon in reinforcement learning that can lead to suboptimal decisions. In summary, Dual Q-Learning enhances the robustness and efficiency of learning in complex environments, providing a more balanced approach to decision-making in uncertain situations.
