Double Q Learning is a model-free reinforcement learning method introduced by Hado van Hasselt in 2010 to address the overestimation bias in standard Q-learning. It improves stability by decoupling the Q-value estimation and action selection processes, ensuring more accurate policy evaluation.
Key Concepts
- Original Problem: Q-learning tends to overestimate action values due to the use of the same Q-function for both target and action selection.
- Solution: Double Q Learning employs two separate Q-networks—one for selecting actions and another for evaluating their values.
- Advantages:
- Reduces overestimation error
- Enhances convergence in complex environments
- Widely used in Deep Q-Networks (DQN)
Applications
This algorithm is foundational in deep reinforcement learning, particularly in scenarios like:
- Game-playing agents (e.g., Atari games)
- Robotics control
- Autonomous systems
Further Reading
For deeper insights, explore:
- Q_learning – The predecessor algorithm
- Deep_Reinforcement_Learning – Advanced techniques and frameworks