entry_point: agents.rlad.agent:Agent
kwargs:
  augmentation_config:
    use_aug: True
    pad: 8
  vehicle_measurements_config:
    entry_point: agents.models.vehicle_measurements.vehicle_measurements:VehicleMeasurementsEncoder
    lr: 0.001
    weight_decay: 0.0
    num_inputs: 2
    fc_dims: 8
    out_dims: 16
  traffic_light_config:
    entry_point: agents.models.traffic_light.traffic_light:TrafficLightDecoder
    lr: 0.001
    weight_decay: 0.0
    update_frequency: 1
    tl_fc_dims: 64
    tl_latent_size: 128
  waypoints_config:
    entry_point: agents.models.waypoints.waypoints:WayConv1D
    num_waypoints: 10
    fc_dims: 20
    out_dims: 32
    lr: 0.001
    weight_decay: 0.0
  image_config:
    entry_point: agents.models.image.image:ImageEncoder
    lr: 0.001 
    weight_decay: 0.000
    image_size: 256
    tau: 0.05
  agent_config:
    lr_alpha: 0.00001
    device: cuda:7
    gamma: 0.99 
    alpha: 0.1 
    automatic_alpha: True
    batch_size: 256
    latent_route_planner_size: 32
    latent_vehicle_measurement_size: 16
    latent_image_size: 256
    target_update_interval: 1
    deque_size: 3
    pid:
      kp: 1.0
      ki: 0.2
      kd: 0.001
      dt: 0.1
    repeat_action: 2
    n_step: 1
  critic_config:
    entry_point: agents.models.rl_networks.critic_network:CriticNetwork
    fc1_dims: 1024 
    fc2_dims: 1024 
    lr: 0.001 
    update_frequency: 1
    tau: 0.01
  actor_config:
    entry_point: agents.models.rl_networks.actor_network:ActorNetwork
    fc1_dims: 1024 
    fc2_dims: 1024
    lr: 0.001 
    log_sig_min: -10 
    log_sig_max: 2
    epsilon: 0.000001 
    update_frequency: 1 
  memory_config:
    capacity: 100000 
    num_workers: 2
    raw_state_info:
      central_rgb:
        shape: (9, 256, 256) 
        dtype: np.uint8
      route_plan:
        shape: (10, 2)
        dtype: np.float32
      vehicle_measurements:
        shape: (6,)
        dtype: np.float32
      traffic_light_state:
        shape: (1,)
        dtype: np.uint8


    