what is difference between the DDQN and DQN?

Answer: The main difference between DDQN (Double Deep Q-Network) and DQN (Deep Q-Network) is that DDQN employs two separate Q-networks to mitigate overestimation of Q-values, addressing a limitation in the original DQN algorithm.

Here’s a detailed explanation of the differences between DDQN (Double Deep Q-Network) and DQN (Deep Q-Network):

Aspect	DQN (Deep Q-Network)	DDQN (Double Deep Q-Network)
Q-Value Estimation	Employs a single Q-network for both target and current Q-values, leading to potential overestimation of Q-values.	Utilizes two separate Q-networks: one for target Q-values and one for current Q-values, mitigating the overestimation issue present in DQN.
Target Q-Value Update	Employs a simple target Q-value update using the maximum Q-value of the next state according to the current Q-network.	Utilizes the target Q-network to select the action for the next state and then calculates the Q-value using the current Q-network. This helps reduce overestimation.
Algorithmic Enhancement	Standard DQN algorithm without addressing overestimation bias.	Specifically designed to address the overestimation bias by introducing the double Q-learning approach, which utilizes two Q-networks.
Performance Improvement	Prone to overestimation, which can result in suboptimal policy learning.	Tends to provide more accurate Q-value estimates, leading to improved stability and better convergence in the learning process.
Implementation Complexity	Simpler implementation with a single Q-network.	Slightly more complex due to the need to manage and update two Q-networks independently.
Original Paper	“Playing Atari with Deep Reinforcement Learning” by Volodymyr Mnih et al. (2013)	“Deep Reinforcement Learning with Double Q-learning” by Hado van Hasselt et al. (2015)

Conclusion:

In summary, DDQN builds upon the DQN architecture by introducing the double Q-learning approach, using two Q-networks to provide more accurate Q-value estimates and address the overestimation bias present in standard DQN. This modification enhances the stability and convergence of the learning process in reinforcement learning scenarios.