suc_count = 0
for epoch in range(current_epoch, epochs_count):
state = env.reset()
episode_reward = 0
epoche_observations = []
epoche_actions = []
epoche_rewards = []
time_begin = time.clock()
while True:
if is_render:
env.render()
action = model.predict(state)
episode_rewards_sum = sum(epoche_rewards)
if episode_rewards_sum < min_reward:
action = 0
if time.clock() - time_begin > time_limit:
action = 0
if suc_count < (epochs_count - current_epoch) / 2:
# replace neural metworc with heuristic algorithm on low vertical coordinate
if state[1] < 0.5 and random.random() > 0.5:
action = heuristic(env, state)
state_, reward, done, info = env.step(action)
for i in range(1, 10):
total_reward = 0
steps = 0
s = env.reset()
epoche_rewards = []
start = time.clock()
print("iteration: ", i)
while True:
env.render()
frames.append(Image.fromarray(env.render(mode='rgb_array')))
if is_heuristic:
a = heuristic(env, s)
else:
a = model.predict(s)
# replace neural metworc with heuristic algorithm on low vertical coordinate
#if s[1] < 0.1:
# a = heuristic(env, s)
state_, reward, done, info = env.step(a)
epoche_rewards.append(reward)
print("reward ", reward, "action ", a)
episode_rewards_sum = sum(epoche_rewards)
if episode_rewards_sum < -200:
done = True
if time.clock() - start > 40:
break
import tensorflow as tf
os.environ["CUDA_VISIBLE_DEVICES"] = str(args.gpu)
model = TFFMRegressor(
order=dim,
rank=args.embedding_dim,
optimizer=tf.train.AdagradOptimizer(learning_rate=args.lr),
n_epochs=args.train_epochs,
# batch_size=1076946,
batch_size=4096,
init_std=0.001,
reg=args.weight_decay,
input_type='sparse',
log_dir=os.path.join(args.save, save_name),
)
model.fit(train_queue[0], train_queue[1], show_progress=True)
inferences = model.predict(test_queue[0])
mse = mean_squared_error(test_queue[1], inferences)
rmse = np.sqrt(mse)
logging.info('rmse: %.4f[%.4f]' % (rmse, time() - start))
elif args.mode == 'autoneural':
start = time()
if dim == 2:
model = AutoNeural(num_users, num_items, args.embedding_dim,
args.weight_decay).cuda()
elif dim == 3:
model = AutoNeural_Triple(num_ps, num_qs, num_rs,
args.embedding_dim,
args.weight_decay).cuda()
embedding_oprimizer = torch.optim.Adagrad(model.embedding_parameters(),
args.lr)