Configure Agent Settings¶

Configure and customize your agent for optimal performance.

Configuration Overview¶

Agent configuration controls how your agent behaves and learns.

Basic Configuration¶

agent:
  name: my-agent
  type: classification
  description: My custom agent
  version: 1.0
  framework: tensorflow  # tensorflow, pytorch, sklearn

Model Architecture¶

Neural Network Configuration¶

model:
  type: neural_network
  layers:
    - type: dense
      units: 128
      activation: relu
    - type: dropout
      rate: 0.3
    - type: dense
      units: 64
      activation: relu
    - type: dense
      units: 32
      activation: relu
    - type: dense
      units: num_classes
      activation: softmax

Alternative Architectures¶

# Convolutional Neural Network (for images)
model:
  type: cnn
  filters: [32, 64, 128]
  kernel_size: 3

# Recurrent Neural Network (for sequences)
model:
  type: rnn
  hidden_units: 128
  num_layers: 2

# Transformer (for NLP)
model:
  type: transformer
  embedding_dim: 768
  num_heads: 8
  num_layers: 6

Training Settings¶

training:
  optimizer: adam
  learning_rate: 0.001
  loss_function: categorical_crossentropy

  epochs: 20
  batch_size: 32
  validation_split: 0.2

  early_stopping: true
  patience: 5
  min_delta: 0.001

Data Configuration¶

data:
  train_file: data/train.csv
  test_file: data/test.csv

  features:
    - column1
    - column2
    - column3
  target: label

  preprocessing:
    lowercase: true
    remove_special_chars: false
    normalize: true
    scale: minmax  # minmax, standard

Advanced Configurations¶

Learning Rate Scheduling¶

training:
  scheduler: cosine_annealing
  scheduler_params:
    t_max: 20
    eta_min: 0.00001

Regularization¶

training:
  dropout: 0.3
  l1_regularization: 0.0001
  l2_regularization: 0.001
  batch_normalization: true

Data Augmentation¶

data:
  augmentation:
    enabled: true
    techniques:
      - rotation: 15
      - zoom: 0.2
      - flip: true

Next Steps¶

Check Common Issues for help.