def run_configuration(argv):
params = ParameterServer()
params["ML"]["BaseAgent"][
"SummaryPath"] = "/home/mansoor/Study/Werkstudent/fortiss/code/bark-ml/summaries"
params["ML"]["BaseAgent"][
"CheckpointPath"] = "/home/mansoor/Study/Werkstudent/fortiss/code/bark-ml/checkpoints"
env = gym.make(FLAGS.env, params=params)
agent = FQFAgent(env=env, test_env=env, params=params)
if FLAGS.load and params["ML"]["BaseAgent"]["CheckpointPath"]:
agent.load_models(
os.path.join(params["ML"]["BaseAgent"]["CheckpointPath"], "best"))
if FLAGS.mode == "train":
agent.train()
elif FLAGS.mode == "visualize":
agent.visualize()
elif FLAGS.mode == "evaluate":
# writes evaluaion data using summary writer in summary path
agent.evaluate()
else:
raise Exception("Invalid argument for --mode")
def main():
args = configure_args()
exp_dir = args.exp_dir or "results/training/toy_evaluation"
params_filename = glob.glob(os.path.join(exp_dir, "params_*"))
params = ParameterServer(filename=params_filename[0])
behavior_params_filename = glob.glob(
os.path.join(exp_dir, "behavior_params*"))
if behavior_params_filename:
splits = 8
behavior_params = ParameterServer(filename=behavior_params_filename[0])
behavior_space = BehaviorSpace(behavior_params)
hypothesis_set, _ = behavior_space.create_hypothesis_set_fixed_split(
split=splits)
observer = BeliefObserver(params, hypothesis_set, splits=splits)
behavior = BehaviorDiscreteMacroActionsML(behavior_params)
else:
behavior = BehaviorDiscreteMacroActionsML(params)
observer = NearestAgentsObserver(params)
evaluator = GoalReached(params)
scenario_params = ParameterServer(
filename=
"configuration/database/scenario_sets/interaction_merging_light_dense_1D.json"
)
scenario_generator = ConfigurableScenarioGeneration(params=scenario_params,
num_scenarios=5)
scenario_file = glob.glob(os.path.join(exp_dir, "scenarios_list*"))
scenario_generator.load_scenario_list(scenario_file[0])
viewer = MPViewer(params=params,
x_range=[-35, 35],
y_range=[-35, 35],
follow_agent_id=True)
env = HyDiscreteHighway(behavior=behavior,
observer=observer,
evaluator=evaluator,
viewer=viewer,
scenario_generation=scenario_generator,
render=True)
env.reset()
actions = [0, 1, 2, 3, 4, 5, 6]
for action in actions:
print(action)
env.step(action)
agent = FQFAgent(env=env, test_env=env, params=params)
agent.load_models(os.path.join(exp_dir, "agent/checkpoints/final"))