Hands-On Reinforcement Learning with Python

Dueling network

Now, we build our dueling DQN; we build three convolutional layers followed by two fully connected layers, and the final fully connected layer will be split into two separate layers for value stream and advantage stream. We will use the aggregate layer, which combines both the value stream and the advantage stream, to compute the q value. The dimensions of these layers are given as follows:

• Layer 1: 32 8x8 filters with stride 4 + RELU
• Layer 2: 64 4x4 filters with stride 2 + RELU
• Layer 3: 64 3x3 filters with stride 1 + RELU
• Layer 4a: 512 unit fully-connected layer + RELU
• Layer 4b: 512 unit fully-connected layer + RELU
• Layer 5a: 1 unit FC + RELU (state value)
• Layer 5b: Actions FC + RELU (advantage value)
• Layer6: Aggregate ...