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import os
import argparse
import pickle
import keras
import numpy as np
STORING_PATH = '../offline_rl_results/'
MODELS_PATH = './trained_models/'
def save_results(environment, approximator, seed, rewards):
storing_path = os.path.join(STORING_PATH, environment, approximator, str(seed))
if not os.path.exists(storing_path):
os.makedirs(storing_path)
np.save(storing_path + '/' + 'upside_down_rewards.npy', rewards)
def save_trained_model(environment, seed, algorithm, model):
storing_path = os.path.join(MODELS_PATH, environment, str(seed), algorithm)
if not os.path.exists(storing_path):
os.makedirs(storing_path)
model.save_weights(storing_path + '/' + 'trained_model.h5')
def save_offline_results(environment, algorithm, seed, returns):
storing_path = os.path.join(STORING_PATH, algorithm, str(seed))
if not os.path.exists(storing_path):
os.makedirs(storing_path)
np.save(storing_path + '/rewards.npy', returns)
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