default_config_mountaincar.yaml

env_name: !!str MountainCar-v0
device: !!str cpu                           # torch device (cuda:0 or cpu)
render_env: !!bool False                    # render environment

agents:
  gtn:
    mode: !!str 'multi'                     # 'single': run on single PC / 'multi': run on multiple PCs
    max_iterations: !!int 50                # maximum number of GTN iterations
    num_threads_per_worker: !!int 1         # how many pytorch/OMP threads per worker
    num_workers: !!int 16                   # size of the population
    noise_std: !!float 5e-2                 # standard deviation of the noise vector
    step_size: !!float 1                    # reptile step size
    nes_step_size: !!bool False             # when set to true, divide step size by the number of workers
    mirrored_sampling: !!bool True          # use normal or mirrored sampling
    num_grad_evals: !!int 1                 # how often to evaluate each gradient
    grad_eval_type: !!str mean              # mean or minmax
    weight_decay: !!float 0.0               # weight decay
    time_mult: !!float 3                    # maximum allocated time as multiplicative of avg. time
    time_max: !!float 300                   # maximum allocated time for first iteration
    time_sleep_master: !!float 0.2          # cycle sleeping time when waiting for data
    time_sleep_worker: !!float 2            # cycle sleeping time when waiting for data
    score_transform_type: !!int 7           # 0-7
    quit_when_solved: !!bool True           # continue training once the environment has been solved?
    synthetic_env_type: !!int 0             # 0: virtual env / 1: reward env
    unsolved_weight: !!float 10000          # penalty weight for all solvable environments if they haven't been solved yet
    agent_name: !!str DDQN                  # which RL agent to use for meta-training

  ddqn_vary:
    vary_hp: !!bool True                    # vary hyperparameters of underlying DDQN algorithm?

  ddqn:
    train_episodes: !!int 1000              # maximum number of episodes to train
    test_episodes: !!int 10                 # maximum number of episodes to test
    init_episodes: !!int 100                # number of episodes to fill the replay buffer
    batch_size: !!int 128                   # batch size when running a policy update step
    gamma: !!float 0.99                     # discount factor
    lr: !!float 1e-3                        # learning rate
    tau: !!float 0.01                       # target network update rate
    eps_init: !!float 1                     # initial random action percentage
    eps_min: !!float 0.01                   # final random action percentage
    eps_decay: !!float 0.99                 # random action decay factor
    rb_size: !!int 100000                   # size of the replay buffer
    same_action_num: !!int 1                # how often to perform the same action subsequently
    activation_fn: !!str relu               # activation function for actor/critic ('tanh', 'relu', 'leakyrelu' or 'prelu')
    hidden_size: !!int 256                  # size of the actor/critic hidden layer
    hidden_layer: !!int 2                   # number of hidden layers
    print_rate: 10                          # update rate of avg meters
    early_out_num: 10                       # based on how many training episodes shall an early out happen
    early_out_virtual_diff: !!float 1e-2    # performance difference for an early out for virtual envs

envs:
  MountainCar-v0:
    solved_reward: !!float -110             # used for early out in RL agent training
    max_steps: !!int 200                    # maximum number of steps per episode
    activation_fn: !!str leakyrelu          # activation function of the virtual environment
    hidden_size: 128                        # size of the hidden layer of the virtual environment
    hidden_layer: !!int 1                   # number of hidden layers of the virtual environment
    info_dim: !!int 0                       # additional information dimension from step function
    reward_env_type: !!int 0                # type of reward shaping function