eval_config.yaml

res_path: examples/experimental/dataframes # Store dataframes here
test_dataset_size: 10_000 # Number of test scenarios to evaluate on

# Environment settings
train_dir: data/processed/training 
test_dir: data/processed/validation
file_prefix: null # Controls the file prefix used when searching files in SceneDataLoader, defaults to tfrecord (WOMD data)

num_worlds: 50 # Number of parallel environments for evaluation
max_controlled_agents: 64 # Maximum number of agents controlled by the model.
ego_state: true
road_map_obs: true
partner_obs: true
norm_obs: true
remove_non_vehicles: true # If false, all agents are included (vehicles, pedestrians, cyclists)
lidar_obs: false # NOTE: Setting this to true currently turns of the other observation types
reward_type: "weighted_combination"
collision_weight: -0.75
off_road_weight: -0.75
goal_achieved_weight: 1.0
dynamics_model: "classic"
collision_behavior: "ignore" # Options: "remove", "stop"
dist_to_goal_threshold: 2.0
polyline_reduction_threshold: 0.1 # Rate at which to sample points from the polyline (0 is use all closest points, 1 maximum sparsity), needs to be balanced with kMaxAgentMapObservationsCount
sampling_seed: 42 # If given, the set of scenes to sample from will be deterministic, if None, the set of scenes will be random
obs_radius: 50.0 # Visibility radius of the agents
init_roadgraph: False
render_3d: True

action_type: "discrete"
# Number of discretizations in the action space
# Note: Make sure that this equals the discretizations that the policy
# has been trained with
action_space_steer_disc: 13  
action_space_accel_disc: 7 

device: "cuda" # Options: "cpu", "cuda"