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 test_agent_wrapping(self):
params = ParameterServer()
env = gym.make("highway-v1", params=params)
env.reset()
# agent = IQNAgent(env=env, test_env=env, params=params)
params["ML"]["BaseAgent"]["MaxEpisodeSteps"] = 2
params["ML"]["BaseAgent"]["NumEvalEpisodes"] = 2
agent = FQFAgent(env=env, params=params)
agent.train_episode()
agent.evaluate()
def test_agent_wrapping(self):
params = ParameterServer()
env = gym.make("highway-v1", params=params)
env.reset()
params["ML"]["BaseAgent"]["MaxEpisodeSteps"] = 2
params["ML"]["BaseAgent"]["NumEvalEpisodes"] = 2
train_bench = TrainingBenchmarkDatabase()
agent = FQFAgent(env=env,
agent_save_dir="./save_dir",
params=params,
training_benchmark=train_bench)
agent.train_episode()
agent.evaluate()