def make_target_env_with_baseline(
observation_scaling=1.0, # 10.0
ego_starting_distance=150.0):
return factored_gym.FactoredGym(
road.RoadProcess(ego_starting_distance=ego_starting_distance),
road.RoadObserver(observation_scaling),
road.RoadTerminator(time_limit=3 * 60), road.RoadGoalRewarder(), [])
def __init__(self, env_config):
observation_scaling = 1.0 # 10.0
ego_starting_distance = 600.0
super().__init__(
road.RoadProcess(ego_starting_distance=ego_starting_distance),
road.RoadObserver(observation_scaling),
road.RoadTerminator(time_limit=3 * 60), road.RoadRewarder(), [])
def __init__(self, env_config):
observation_scaling = 1.0 # 10.0
ego_starting_distance = 5.0
super().__init__(
road.RoadProcess(ego_starting_distance=ego_starting_distance),
road.RoadObserver(observation_scaling),
road.RoadTerminator(time_limit=5 * 60), road.RoadGoalRewarder(),
[factored_gym.ActionCenterer([.001, 5], [0, 0])])
def __init__(self, env_config=None):
observation_scaling = 1.0 # 10.0
ego_starting_distance = 30.0
# scenario_file = "/home/pgraf/work/cavs/CarlaProjects/carla-collect-2_28_20/./docs/scenarios/town01/scenario_009.json" #13=has intersection, 9=turn, no intersection
# process = RoadProcessOnCarlaMap(scenario_file, ego_starting_distance=ego_starting_distance, traffic_density=0.00, target_inset=15) #0.02
# process =road.RoadProcess(ego_starting_distance=ego_starting_distance),
# scenario_file = "/home/pgraf/work/cavs/CarlaProjects/cavs-environments_k_road_roadmaker/cavs_environments/vehicle/k_road/scenario/road/example/multi_route_scenario_000b.json"
# Learned to drive this with lidar (even w/out vehicle state in obs)
scenario_file = "/home/pgraf/work/cavs/CarlaProjects/cavs-environments_roadmaker-clean/cavs_environments/vehicle/k_road/scenario/road/example/multi_route_scenario_town01_all.json"
route_num = 7
# scenario_file = "/home/pgraf/work/cavs/CarlaProjects/cavs-environments_roadmaker-clean/cavs_environments/vehicle/k_road/scenario/road/example/multi_route_scenario_town04_merge.json"
# route_num = 0
process = KRoadProcessOnCarlaMap(
scenario_file,
traffic_density=0.00,
target_inset=70,
ego_starting_distance=ego_starting_distance,
fixed_route_num=route_num)
# process = KRoadCarlaTwinRoadProcess(scenario_file, mode=KCModes.K0, traffic_density=0.00, target_inset=0,
# ego_starting_distance=ego_starting_distance, fixed_route_num = route_num)
# speed_mean=12,
super().__init__(
process,
# road.RoadObserver(observation_scaling),
RoadOccupancyGridObserver(observation_scaling,
grid_length=9,
grid_width=5),
# RoadOccupancyGridObserver(observation_scaling, grid_length = 18, grid_width = 10), #, frames_to_observe=1),
road.RoadTerminator(time_limit=60), # (time_limit=3 * 60),
road.RoadRewarder(),
# road.RoadOccupancyGridRewarder()
)
def __init__(self, env_config=None):
observation_scaling = 1.0 # 10.0
ego_starting_distance = 2000.0
print("env_config", env_config)
if "use_factored_obs" in env_config and env_config['use_factored_obs']:
obs_config = env_config['obs_config']
observer = FactoredRoadObserver(obs_config)
else:
observer = road.RoadObserver(observation_scaling)
# RoadOccupancyGridObserver(observation_scaling, grid_length = 9, grid_width = 5),
# RoadOccupancyGridObserver(observation_scaling, grid_length = 18, grid_width = 10), #, frames_to_observe=1),
scenario_file = "/home/pgraf/work/cavs/CarlaProjects/cavs-environments_roadmaker-clean/cavs_environments/vehicle/k_road/scenario/road/example/multi_route_scenario_town04_jct.json"
scenario = "cross_intersection"
with_carla = False
if "scenario" in env_config:
scenario = env_config['scenario']
if "with_carla" in env_config:
with_carla = env_config['with_carla']
if scenario == "curvy_road":
bot_routes = None
ego_routes = [2]
traffic_density = 0
target_inset = 20
ego_starting_distance = 65.0
elif scenario == "avoid_bots":
bot_routes = [1]
ego_routes = [1]
traffic_density = 3
target_inset = 5
ego_starting_distance = 2000.0
elif scenario == "cross_intersection":
bot_routes = [1]
ego_routes = [0]
traffic_density = 12
target_inset = 5
ego_starting_distance = 2000.0
# scenario_file = "/home/pgraf/work/cavs/CarlaProjects/carla-collect-2_28_20/./docs/scenarios/town01/scenario_009.json" #13=has intersection, 9=turn, no intersection
# process = RoadProcessOnCarlaMap(scenario_file, ego_starting_distance=ego_starting_distance, traffic_density=0.00, target_inset=15) #0.02
# process =road.RoadProcess(ego_starting_distance=ego_starting_distance),
# scenario_file = "/home/pgraf/work/cavs/CarlaProjects/cavs-environments_k_road_roadmaker/cavs_environments/vehicle/k_road/scenario/road/example/multi_route_scenario_000b.json"
# Learned to drive this with lidar (even w/out vehicle state in obs)
# scenario_file = "/home/pgraf/work/cavs/CarlaProjects/cavs-environments_roadmaker-clean/cavs_environments/vehicle/k_road/scenario/road/example/multi_route_scenario_town01_all-nobndry.json"
# route_num = 7
# scenario_file = "/home/pgraf/work/cavs/CarlaProjects/cavs-environments_roadmaker-clean/cavs_environments/vehicle/k_road/scenario/road/example/multi_route_scenario_town04_merge.json"
# scenario_file = "/home/pgraf/work/cavs/CarlaProjects/cavs-environments_roadmaker-clean/cavs_environments/vehicle/k_road/scenario/road/example/multi_route_scenario_town04_local.json"
route_num = 1 ## only draw this route even though there are more "paths"
# route_num = None ## draws all routes, ego goes on 0, no control over which that is
if not with_carla:
process = KRoadProcessOnCarlaMap(
scenario_file,
traffic_density=traffic_density,
speed_mean=8,
target_inset=target_inset,
ego_starting_distance=ego_starting_distance,
ego_routes=ego_routes,
bot_routes=bot_routes)
else:
process = KRoadCarlaTwinRoadProcess(
scenario_file,
mode=KCModes.KC,
traffic_density=traffic_density,
speed_mean=8,
target_inset=target_inset,
ego_starting_distance=ego_starting_distance,
ego_routes=ego_routes,
bot_routes=bot_routes)
# process = KRoadCarlaTwinRoadProcess(scenario_file, mode=KCModes.K0, traffic_density=0.00, target_inset=0,
# ego_starting_distance=ego_starting_distance, fixed_route_num = route_num)
# speed_mean=12,
# target_inset = 70
super().__init__(
process,
observer,
road.RoadTerminator(time_limit=60), # (time_limit=3 * 60),
road.RoadRewarder(),
# road.RoadOccupancyGridRewarder()
)