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.
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