# seed for the experiment
seed: 42
# device for the rl-agent
device: 'cuda:0'

policy:
  init_noise_std: 1.0
  actor_hidden_dims: [256, 128, 64]
  critic_hidden_dims: [256, 128, 64]
  activation: "elu"  # can be elu, relu, selu, crelu, lrelu, tanh, sigmoid

  # only required when "policy_class_name" is'ActorCriticRecurrent':
  # rnn_type: 'lstm'
  # rnn_hidden_size: 512
  # rnn_num_layers: 1

algorithm:
  # training params
  value_loss_coef: 1.0
  use_clipped_value_loss: True
  # Seems to be the same as clip_range
  clip_param: 0.2
  # Seems to be the same as ent_coef
  entropy_coef: 0.01
  # ? seems to be the same as n_epochs
  num_learning_epochs: 8
  # seems to be the same as batch_size
  num_mini_batches: 64  # mini batch size: num_envs * nsteps / nminibatches
  # seems to be same as learning_rate
  learning_rate: 3.0e-4  # 5.e-4
  schedule: "adaptive"  # adaptive, fixed
  # same as gamma
  gamma: 0.99
  # seems to be the same as gae_lambda
  lam: 0.95
  # seems to be the same as target_kl
  desired_kl: 0.01
  # seems to be the same as max_grad_norm
  max_grad_norm: 1.0

runner:
  policy_class_name: "ActorCritic"
  algorithm_class_name: "PPO"
  num_steps_per_env: 64  # per iteration
  max_iterations: 250  # number of policy updates

  # logging
  save_interval: 50  # check for potential saves every this many iterations
  experiment_name: "reach"
  run_name: ""
  # load and resume
  resume: False
  load_run: -1  # -1: last run
  checkpoint: -1  # -1: last saved model
  resume_path: None  # updated from load_run and chkpt
