`unreal.LearningAgentsImitationTrainerTrainingSettings`¶

class unreal.LearningAgentsImitationTrainerTrainingSettings¶

The configurable settings for the training process.

C++ Source:

Editor Properties: (see get_editor_property/set_editor_property)

batch_size (uint32): [Read-Write] Batch size to use for training. Smaller values tend to produce better results at the cost of slowing down training. Large batch sizes are much more computationally efficient when training on the GPU.
device (LearningAgentsTrainerDevice): [Read-Write] The device to train on.
learning_rate (float): [Read-Write] Learning rate of the policy network. Typical values are between 0.001 and 0.0001.
learning_rate_decay (float): [Read-Write] Ratio by which to decay the learning rate every 1000 iterations.
number_of_iterations (int32): [Read-Write] The number of iterations to run before ending training.
random_seed (int32): [Read-Write] The seed used for any random sampling the trainer will perform, e.g. for weight initialization.
use_tensorboard (bool): [Read-Write] If true, TensorBoard logs will be emitted to the intermediate directory.
weight_decay (float): [Read-Write] Amount of weight decay to apply to the network. Larger values encourage network weights to be smaller but too large a value can cause the network weights to collapse to all zeros.