def train_ddpg_all_with_lr_drop(num_frames, third=False):
DDPGAgent.train(num_frames)
Settings.LEARNING_RATE /= 10
old_log_dir = Settings.FULL_LOG_DIR
Settings.LOG_DIR = Settings.LOG_DIR + "_extended"
Settings.setup_logging()
DDPGAgent.resume_training(old_log_dir, num_frames)
if third:
Settings.TASK = "EVALUATE_DDPG"
Settings.MODEL_NAME = Settings.FULL_LOG_DIR
eval_agent = DDPGAgent.load(Settings.FULL_LOG_DIR)
eval_agent.evaluate(Settings.NUM_EPISODES)
Settings.TASK = "TRAIN_DDPG"
Settings.LEARNING_RATE /= 10
old_log_dir = Settings.FULL_LOG_DIR
Settings.LOG_DIR = Settings.LOG_DIR + "2"
Settings.setup_logging()
DDPGAgent.resume_training(old_log_dir, num_frames)
Settings.TASK = "EVALUATE_DDPG"
Settings.MODEL_NAME = Settings.FULL_LOG_DIR
eval_agent = DDPGAgent.load(Settings.FULL_LOG_DIR)
eval_agent.evaluate(Settings.NUM_EPISODES)
for i, key in enumerate(keys):
setattr(Settings, key, value_tuple[i])
if not Settings.TEST_ROLLOUT_STATE and Settings.ST_TEST_ROLLOUTS != 2:
continue
if Settings.ROLLOUT_LENGTH == 1 and Settings.ST_TEST_ROLLOUTS != 2:
continue
if Settings.ST_TEST_ROLLOUTS > Settings.ROLLOUT_LENGTH:
continue
do_task()
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("config", nargs='?', default=None)
args = parser.parse_args()
config_file = args.config
if config_file is not None:
Settings.load_from_file(config_file)
Settings.setup_logging()
merge_gym.register_environments()
if Settings.SEED != "Random":
np.random.seed(Settings.SEED)
torch.manual_seed(Settings.SEED)
random.seed(Settings.SEED)
if Settings.CUDA:
torch.backends.cudnn.deterministic = True
torch.backends.cudnn.benchmark = False
do_task()
