Dueling Network Architectures

Dueling Network Architectures improve reinforcement learning by splitting decision-making into two streams: one estimates the value of being in a specific state, and the other assesses the advantage of each possible action in that state. These are then combined to produce better estimates of which actions to take. This approach allows the network to more efficiently learn which states are valuable, regardless of the specific actions, leading to more accurate decision-making in complex environments. It enhances learning speed and stability, especially when many actions share similar benefits.