investment_euler_simple.yaml

trainer:
  max_epochs: 100
  min_epochs: 0
  max_time: 00:00:15:00
  precision: 32
  num_sanity_val_steps: 0
  logger: null
  callbacks:
    - class_path: pytorch_lightning.callbacks.EarlyStopping
      init_args:
        monitor: val_loss
        min_delta: 0.0
        patience: 50
        mode: min
        check_finite: true
        divergence_threshold: 100000 # stops if larger
        stopping_threshold: 1.0e-6   # typically the binding stopping threshold
optimizer:
  class_path: torch.optim.Adam
  init_args:
    lr: 1.0e-3

# Scheduler currently not tuned
lr_scheduler:
  class_path: torch.optim.lr_scheduler.StepLR
  init_args:
    step_size: 50 # number of epochs
    gamma: 0.8
# lr_scheduler:
#   class_path: torch.optim.lr_scheduler.ReduceLROnPlateau
#   init_args:
#     factor: 0.1
#     mode: min
#     patience: 5
model:
  # Model parameters
  N: 128
  alpha_0: 1.0
  alpha_1: 1.0
  beta: 0.95
  gamma: 90.0
  sigma: 0.005
  delta: 0.05
  eta: 0.001
  nu: 1.0

  # Settings for output
  verbose: false
  hpo_objective_name: test_loss
  always_log_hpo_objective: false  
  print_metrics: false
  save_metrics: false
  save_test_results: false


  # Settings for method
  omega_quadrature_nodes: 7
  normalize_shock_vector: true
  reset_trajectories_frequency: 0 # in epochs, 0 to never reset
  train_trajectories: 16
  val_trajectories: 8
  test_trajectories: 32
  batch_size: 16 # set to 0 for full dataset
  shuffle_training: true
  T: 63
  X_0_loc: 0.9
  X_0_scale: 0.05

  # Settings for deep sets neural networks
  ml_model:
    class_path: econ_layers.layers.DeepSet
    init_args:
      n_in: 1
      n_out: 1
      L: 4

      phi_layers: 1
      phi_hidden_dim: 128
      phi_hidden_bias: false
      phi_last_bias: true
      phi_activator:
        class_path: torch.nn.ReLU

      rho_layers: 4
      rho_hidden_dim: 256
      rho_hidden_bias: true
      rho_last_bias: true
      rho_activator:
        class_path: torch.nn.ReLU