Reinforcement Learning Algorithms with Python

Model architecture

So far, we have talked about the algorithm itself, but we haven't explained the architecture of the DQN. Besides the new ideas that have been adopted to stabilize its training, the architecture of the DQN plays a crucial role in the final performance of the algorithm. In the DQN paper, a single model architecture is used in all of the Atari environments. It combines CNNs and FNNs. In particular, as observation images are given as input, it employs a CNN to learn about feature maps from those images. CNNs have been widely used with images for their translation invariance characteristics and for their property of sharing weights, which allows the network to learn with fewer weights compared to other deep neural network types. ...