exp_prefix: psro_rn
env_config:
  lib: expground.envs.poker
  env_id: leduc_poker
  params:
    fixed_player: True
    players: 2

learner_config:
  type: psro
  params:
    seed: 1
    meta_solver: fictitious_play
    use_learnable_dist: False
    episodic_training: False
    train_every: 10
    learning_mode: off_policy
    ray_mode: True
    mixed_at_every_step: False
    evaluation_worker_num: 30
  stop_conditions:
    max_iteration: 100
  inner_stop_conditions:
    max_episode: 10000

# ============ copy from dqn yaml ===============
rollout_config:
  type: sequential
  fragment_length: 100  # maximum time steps of one batched data, should lower than total_timesteps
  max_step: 20  # for simulation
  num_simulation: 20

training_config:
  actor_lr: 0.0003
  critic_lr: 0.0003
  optimizer: Adam
  batch_size: 32
  gamma: 1.0
  tau: 0.01
  grad_norm_clipping: 0.5
  total_timesteps: 100000
  exploration_fraction: 0.1
  exploration_final_eps: 0.1
  auto_alpha: False

sampler_config:
  sampler_type: simple
  params:
    capacity: 10000
    learning_starts: 1000

algorithm:
  name: SAC
  policy: sac.SAC
  trainer: sac.SACTrainer
  loss: sac.SACLoss

model_config:
  actor:
    network: mlp
    layers:
      - units: 256
        activation: ReLU
      - units: 256
        activation: ReLU
      # - units: 256
      #   activation: ReLU
      # - units: 256
      #   activation: ReLU
    output:
      activation: False
  critic:
    network: mlp
    layers:
      - units: 256
        activation: ReLU
      - units: 256
        activation: ReLU
      # - units: 256
      #   activation: ReLU
      # - units: 256
      #   activation: ReLU
    output:
      activation: False

  auto_alpha: False
  initial_alpha: 0.01

custom_config:
  use_cuda: False
