def get_predictor(self, trainer, environment):
state_preprocessor = Preprocessor(environment.normalization, False)
action_preprocessor = Preprocessor(environment.normalization_action,
False)
q_network = trainer.q_network
dqn_with_preprocessor = ParametricDqnWithPreprocessor(
q_network.cpu_model().eval(), state_preprocessor,
action_preprocessor)
serving_module = ParametricDqnPredictorWrapper(
dqn_with_preprocessor=dqn_with_preprocessor)
predictor = ParametricDqnTorchPredictor(serving_module)
return predictor
def _test_parametric_dqn_workflow(self,
use_gpu=False,
use_all_avail_gpus=False):
"""Run Parametric DQN workflow to ensure no crashes, algorithm correctness
not tested here."""
with tempfile.TemporaryDirectory() as tmpdirname:
lockfile = os.path.join(tmpdirname, "multiprocess_lock")
Path(lockfile).touch()
params = {
"training_data_path":
os.path.join(
curr_dir,
"test_data/parametric_action/cartpole_training.json.bz2"),
"eval_data_path":
os.path.join(
curr_dir,
"test_data/parametric_action/cartpole_eval.json.bz2"),
"state_norm_data_path":
os.path.join(
curr_dir,
"test_data/parametric_action/state_features_norm.json"),
"action_norm_data_path":
os.path.join(curr_dir,
"test_data/parametric_action/action_norm.json"),
"model_output_path":
tmpdirname,
"use_gpu":
use_gpu,
"use_all_avail_gpus":
use_all_avail_gpus,
"init_method":
"file://" + lockfile,
"num_nodes":
1,
"node_index":
0,
"epochs":
1,
"rl": {},
"rainbow": {},
"training": {
"minibatch_size": 128
},
}
parametric_dqn_workflow.main(params)
predictor_files = glob.glob(tmpdirname + "/model_*.torchscript")
assert len(
predictor_files) == 1, "Somehow created two predictor files!"
predictor = ParametricDqnTorchPredictor(
torch.jit.load(predictor_files[0]))
test_float_state_features = [{
"0": 1.0,
"1": 1.0,
"2": 1.0,
"3": 1.0
}]
test_action_features = [{"4": 0.0, "5": 1.0}]
q_values = predictor.predict(test_float_state_features,
test_action_features)
assert len(q_values[0].keys()) == 1