def test_ddpg_workflow(self): """Run DDPG workflow to ensure no crashes, algorithm correctness not tested here.""" params = { "training_data_path": os.path.join( curr_dir, "test_data/continuous_action/pendulum_training_data.json"), "state_norm_data_path": os.path.join( curr_dir, "test_data/continuous_action/state_features_norm.json"), "action_norm_data_path": os.path.join(curr_dir, "test_data/continuous_action/action_norm.json"), "model_output_path": None, "use_gpu": False, "epochs": 1, "rl": {}, "rainbow": {}, "shared_training": { "minibatch_size": 16 }, "actor_training": {}, "critic_training": {}, } predictor = ddpg_workflow.train_network(params) test_float_state_features = [{"0": 1.0, "1": 1.0, "2": 1.0, "3": 1.0}] test_int_state_features = [{}] action = predictor.actor_prediction(test_float_state_features, test_int_state_features) assert len(action) == 1
def _test_ddpg_workflow(self, use_gpu=False, use_all_avail_gpus=False): """Run DDPG 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/continuous_action/pendulum_training.json.bz2" ), "eval_data_path": os.path.join( curr_dir, "test_data/continuous_action/pendulum_eval.json.bz2" ), "state_norm_data_path": os.path.join( curr_dir, "test_data/continuous_action/state_features_norm.json" ), "action_norm_data_path": os.path.join( curr_dir, "test_data/continuous_action/action_norm.json" ), "model_output_path": tmpdirname, "use_gpu": use_gpu, "use_all_avail_gpus": use_all_avail_gpus, "epochs": 1, "rl": {}, "rainbow": {}, "shared_training": {"minibatch_size": 128}, "actor_training": {}, "critic_training": {}, } predictor = ddpg_workflow.train_network(params) test_float_state_features = [{"0": 1.0, "1": 1.0, "2": 1.0, "3": 1.0}] test_int_state_features = [{}] action = predictor.actor_prediction( test_float_state_features, test_int_state_features ) assert len(action) == 1