def _test_dqn_workflow(self, use_gpu=False, use_all_avail_gpus=False):
"""Run 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/discrete_action/cartpole_training.json.bz2"
),
"eval_data_path": os.path.join(
curr_dir, "test_data/discrete_action/cartpole_eval.json.bz2"
),
"state_norm_data_path": os.path.join(
curr_dir, "test_data/discrete_action/cartpole_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,
"actions": ["0", "1"],
"epochs": 1,
"rl": {},
"rainbow": {"double_q_learning": False, "dueling_architecture": False},
"training": {"minibatch_size": 128},
}
dqn_workflow.main(params)
predictor_files = glob.glob(tmpdirname + "/predictor_*.c2")
assert len(predictor_files) == 1, "Somehow created two predictor files!"
predictor = DQNPredictor.load(predictor_files[0], "minidb")
test_float_state_features = [{"0": 1.0, "1": 1.0, "2": 1.0, "3": 1.0}]
q_values = predictor.predict(test_float_state_features)
assert len(q_values[0].keys()) == 2
def _test_dqn_workflow(self, use_gpu=False, use_all_avail_gpus=False):
"""Run DQN workflow to ensure no crashes, algorithm correctness
not tested here."""
with tempfile.TemporaryDirectory() as tmpdirname:
params = {
"training_data_path": os.path.join(
curr_dir, "test_data/discrete_action/cartpole_training.json.bz2"
),
"eval_data_path": os.path.join(
curr_dir, "test_data/discrete_action/cartpole_eval.json.bz2"
),
"state_norm_data_path": os.path.join(
curr_dir, "test_data/discrete_action/cartpole_norm.json"
),
"model_output_path": tmpdirname,
"use_gpu": use_gpu,
"use_all_avail_gpus": use_all_avail_gpus,
"actions": ["0", "1"],
"epochs": 1,
"rl": {},
"rainbow": {},
"training": {"minibatch_size": 128},
}
predictor = dqn_workflow.main(params)
test_float_state_features = [{"0": 1.0, "1": 1.0, "2": 1.0, "3": 1.0}]
q_values = predictor.predict(test_float_state_features)
assert len(q_values[0].keys()) == 2