train_example.yml

# Note: See src/py/config.py for docs.
name: train
seed: null
save_path: experiments/
agent:
  type: dqn
  config:
    model:
      state_encoder:
        input_units:
          room_status: [64]
          team_status: [64]
          volatile: [128]
          basic: [64]
          species: [128]
          types: [128]
          stats: [128]
          ability: [128]
          item: [128]
          moves: [128]
        active_units: [256]
        bench_units: [256]
        global_units: [256]
        move_attention: [4, 32]
        move_pooling_type: attention
        move_pooling_attention: [4, 32]
        bench_attention: [8, 32]
        bench_pooling_type: attention
        bench_pooling_attention: [8, 32]
        use_layer_norm: true
        std_init: 0.5 # NoisyNet.
      q_value:
        move_units: [256]
        switch_units: [256]
        state_units: [256] # Dueling.
        dist: 51
        use_layer_norm: true
        std_init: 0.5 # NoisyNet.
    # Uncomment if not using NoisyNet.
    #exploration:
    #  decay_type: linear
    #  start: 1.0
    #  end: 0.1
    #  episodes: 5_000
    experience:
      n_steps: 2
      discount_factor: 0.99
      buffer_size: 5000
      priority:
        exponent: 0.5
        importance: 0.4
    learn:
      optimizer:
        class_name: Adam
        config:
          learning_rate: 1.e-4
        module: keras.optimizers
      buffer_prefill: 500
      batch_size: 64
      steps_per_update: 2
      steps_per_target_update: 5000
      steps_per_histogram: 5000
rollout:
  num_episodes: 10_000
  eps_per_eval: 1000
  eps_per_ckpt: 1000
  eps_per_prev_update: 1000
  env:
    max_turns: 100
    batch_limit: 1
    pool:
      workers: 1
      per_worker: 1
      battles_per_log: 1000
      worker_timeout_ms: 60_000 # 1m
      sim_timeout_ms: 300_000 # 5m
    state_type: numpy
  opponents:
    - name: previous
      prob: 0.3
      type: model
      model: previous
eval:
  env:
    max_turns: 100
    batch_limit: 4
    pool:
      workers: 4
      per_worker: 2
      battles_per_log: 100
      worker_timeout_ms: 60_000 # 1m
      sim_timeout_ms: 300_000 # 5m
    state_type: tensor
  opponents:
    - name: previous
      battles: 100
      type: model
      model: previous
    - name: random
      battles: 100
      type: random
    - name: random_move
      battles: 100
      type: random_move
    - name: max_damage
      battles: 100
      type: max_damage