seed: 42

# Models are instantiated using skrl's model instantiator utility
# https://skrl.readthedocs.io/en/latest/api/utils/model_instantiators.html
models:
  separate: False
  policy:  # see skrl.utils.model_instantiators.torch.gaussian_model for parameter details
    clip_actions: True
    clip_log_std: True
    initial_log_std: 0
    min_log_std: -20.0
    max_log_std: 2.0
    input_shape: "Shape.STATES"
    hiddens: [32, 32]
    hidden_activation: ["elu", "elu"]
    output_shape: "Shape.ACTIONS"
    output_activation: "tanh"
    output_scale: 1.0
  value:  # see skrl.utils.model_instantiators.torch.deterministic_model for parameter details
    clip_actions: False
    input_shape: "Shape.STATES"
    hiddens: [32, 32]
    hidden_activation: ["elu", "elu"]
    output_shape: "Shape.ONE"
    output_activation: ""
    output_scale: 1.0


# PPO agent configuration (field names are from PPO_DEFAULT_CONFIG)
# https://skrl.readthedocs.io/en/latest/api/agents/ppo.html
agent:
  rollouts: 16
  learning_epochs: 8
  mini_batches: 1
  discount_factor: 0.99
  lambda: 0.95
  learning_rate: 3.e-4
  learning_rate_scheduler: "KLAdaptiveLR"
  learning_rate_scheduler_kwargs:
    kl_threshold: 0.008
  state_preprocessor: "RunningStandardScaler"
  state_preprocessor_kwargs: null
  value_preprocessor: "RunningStandardScaler"
  value_preprocessor_kwargs: null
  random_timesteps: 0
  learning_starts: 0
  grad_norm_clip: 1.0
  ratio_clip: 0.2
  value_clip: 0.2
  clip_predicted_values: True
  entropy_loss_scale: 0.0
  value_loss_scale: 2.0
  kl_threshold: 0
  rewards_shaper_scale: 1.0
  # logging and checkpoint
  experiment:
    directory: "cartpole_direct"
    experiment_name: ""
    write_interval: 16
    checkpoint_interval: 80


# Sequential trainer
# https://skrl.readthedocs.io/en/latest/api/trainers/sequential.html
trainer:
  timesteps: 1600
  environment_info: "log"
