Hands-On Deep Learning for Games

Expanding network architecture

Actor-Critic architectures increase the complexity of the problem, and thus the complexity and size of the networks needed to solve them. This is really no different than the case in our earlier look at PilotNet, the multilayer CNN architecture that was used by Nvidia to self-drive.

What we want to see is the immediate effect that increasing the size of our network has on a complex example such as the Walker example. Open Unity to the Walker example and complete the following exercise:

1. Open trainer_config.yaml from where it is normally located.
2. Modify the WalkerLearning configuration, as shown in the following code:

WalkerLearning:    normalize: true    num_epoch: 3    time_horizon: 1000    batch_size: 2048 buffer_size: ...