taskid, random_state=run_id)
# nn with embedding related
nn_with_embedding = NN_with_EntityEmbedding(
X_train,
y_train_int,
categorical_features,
categorical_names,
class_names,
epochs=epochs,
batch_size=batch_size,
)
nn_with_embedding_loss, nn_with_embedding_score = nn_with_embedding.evaluate(
X_test, y_test_int)
print("nn_with_embedding prediction score: ", str(nn_with_embedding_score))
logger.log('nn_with_embedding', taskid, run_id, nn_with_embedding_score)
# nn related
nn = NN(
X_train,
y_train_int,
categorical_features,
categorical_names,
class_names,
epochs=epochs,
batch_size=batch_size,
)
nn_loss, nn_score = nn.evaluate(X_test, y_test_int)
print("nn prediction score: ", str(nn_score))
logger.log('nn', taskid, run_id, nn_score)
batch_ac.append(ac)
batch_rcw.append((row, col-w, w-prev_w))
batch_frames.append(frames)
batch_prev_frames = np.array(batch_prev_frames)
batch_ac = np.array(batch_ac)
batch_rcw = np.array(batch_rcw)[:, None, :]
batch_frames = np.array(batch_frames)
q_map._optimize(batch_prev_frames, batch_ac, batch_rcw, batch_frames, batch_dones, batch_weights)
if t % args.target == 0:
q_map.update_target()
if t % 50 == 0:
losses = []
all_images = []
for i_level in range(len(test_levels)):
pred_qmaps = q_map.compute_q_values(test_obs[i_level])
true_qmaps = test_qmaps[i_level]
loss = np.mean((pred_qmaps - true_qmaps)**2)
losses.append(loss)
ob_images = np.concatenate(test_obs[i_level][image_indexes[i_level]], axis=1)
pred_images = np.concatenate((color_map(pred_qmaps[image_indexes[i_level]].max(3))[:, :, :, :3] * 255).astype(np.uint8), axis=1)
true_images = np.concatenate((color_map(true_qmaps[image_indexes[i_level]].max(3))[:, :, :, :3] * 255).astype(np.uint8), axis=1)
all_images.append(np.concatenate((ob_images, true_images, pred_images), axis=0))
img = np.concatenate(all_images, axis=0)
toimage(img, cmin=0, cmax=255).save('{}/images/{}.png'.format(path, t))
if args.render:
img = np.repeat(np.repeat(img, 3, 0), 3, 1)
viewer.imshow(img)
print(t*args.batch, 'Losses:', *losses)
loss_logger.log(t, *losses)
class Q_Map_DQN_Agent(Agent):
def __init__(
self,
# All
observation_space,
n_actions,
coords_shape,
double_replay_buffer,
task_gamma,
exploration_schedule,
seed,
learning_starts=1000,
train_freq=1,
print_freq=100,
env_name='ENV',
agent_name='AGENT',
renderer_viewer=True,
# DQN:
dqn_q_func=None,
dqn_lr=5e-4,
dqn_batch_size=32,
dqn_optim_iters=1,
dqn_target_net_update_freq=500,
dqn_grad_norm_clip=100,
dqn_double_q=True,
# Q-Map:
q_map_model=None,
q_map_random_schedule=None,
q_map_greedy_bias=0.5,
q_map_timer_bonus=0.5,
q_map_lr=5e-4,
q_map_gamma=0.9,
q_map_n_steps=1,
q_map_batch_size=32,
q_map_optim_iters=1,
q_map_target_net_update_freq=500,
q_map_min_goal_steps=10,
q_map_max_goal_steps=20,
q_map_grad_norm_clip=1000,
q_map_double_q=True):
# All
self.observation_space = observation_space
self.n_actions = n_actions
self.coords_shape = coords_shape
self.double_replay_buffer = double_replay_buffer
self.task_gamma = task_gamma
self.exploration_schedule = exploration_schedule
self.learning_starts = learning_starts
self.train_freq = train_freq
self.print_freq = print_freq
agent_name += '-train' + str(train_freq)
# DQN
if dqn_q_func is not None:
self.use_dqn = True
agent_name += '-'
agent_name += 'DQN-lr' + str(dqn_lr) + '-freq-' + str(train_freq)
self.dqn_target_net_update_freq = dqn_target_net_update_freq
self.dqn = DQN(model=dqn_q_func,
observation_space=observation_space,
n_actions=n_actions,
gamma=task_gamma,
lr=dqn_lr,
replay_buffer=double_replay_buffer,
batch_size=dqn_batch_size,
optim_iters=dqn_optim_iters,
grad_norm_clip=dqn_grad_norm_clip,
double_q=dqn_double_q)
else:
self.use_dqn = False
# Q-MAP
if q_map_model is not None:
agent_name += '-'
agent_name += 'Q-MAP-' + q_map_model.description + '-' + str(
q_map_min_goal_steps
) + '-' + str(q_map_max_goal_steps) + '-gamma' + str(
q_map_gamma) + '-lr' + str(q_map_lr) + '-bias' + str(
q_map_greedy_bias) + '-bonus' + str(q_map_timer_bonus)
self.use_q_map = True
self.q_map_timer_bonus = q_map_timer_bonus
self.using_q_map_starts = 2 * self.learning_starts
self.q_map_random_schedule = q_map_random_schedule
self.q_map_greedy_bias = q_map_greedy_bias
self.q_map_goal_proba = 1 # TODO
self.q_map_gamma = q_map_gamma
self.q_map_target_net_update_freq = q_map_target_net_update_freq
self.q_map_min_goal_steps = q_map_min_goal_steps
self.q_map_max_goal_steps = q_map_max_goal_steps
self.q_map_min_q_value = q_map_gamma**(q_map_max_goal_steps - 1)
self.q_map_max_q_value = q_map_gamma**(q_map_min_goal_steps - 1)
self.q_map_goal = None
self.q_map_goal_timer = 0
self.q_map = Q_Map(model=q_map_model,
observation_space=observation_space,
coords_shape=coords_shape,
n_actions=n_actions,
gamma=q_map_gamma,
n_steps=q_map_n_steps,
lr=q_map_lr,
replay_buffer=double_replay_buffer,
batch_size=q_map_batch_size,
optim_iters=q_map_optim_iters,
grad_norm_clip=q_map_grad_norm_clip,
double_q=q_map_double_q)
else:
self.use_q_map = False
if not self.use_dqn and not self.use_q_map:
agent_name += 'random'
else:
self.tf_saver = tf.train.Saver()
agent_name += '-memory' + str(double_replay_buffer._maxsize)
# All
home = os.path.expanduser('~')
sub_name = 'seed-{}_{}'.format(
seed,
datetime.utcnow().strftime('%F_%H-%M-%S-%f'))
self.path = '{}/results/q-map/{}/{}/{}'.format(home, env_name,
agent_name, sub_name)
# log exploration for debugging
exploration_labels = [
'steps', 'planned exploration', 'current exploration',
'random actions', 'goal actions', 'greedy actions'
]
self.exploration_logger = CSVLogger(exploration_labels,
self.path + '/exploration')
# videos etc.
self.renderer = Q_Map_Renderer(self.path, viewer=renderer_viewer)
# path to store
self.tensorflow_path = self.path + '/tensorflow'
if not os.path.exists(self.tensorflow_path):
os.makedirs(self.tensorflow_path)
U.initialize()
self.t = 0
self.episode_rewards = []
self.random_proba = self.exploration_schedule.value(0)
self.random_freq = self.exploration_schedule.value(0)
self.greedy_freq = 1.0 - self.random_freq
self.goal_freq = 0.0
if self.use_dqn:
self.dqn.update_target()
self.seed(seed)
def seed(self, seed):
self.np_random, seed = seeding.np_random(seed)
if self.use_dqn:
self.dqn.seed(seed)
if self.use_q_map:
self.q_map.seed(seed)
return [seed]
def reset(self, ob):
if self.use_q_map:
self.q_map_goal_timer = 0
self.q_map_goal = None
frames = ob[0]
ac = self.choose_action(ob)
self.log()
self.episode_rewards.append(0.0)
self.prev_ob = ob
self.prev_ac = ac
return ac
def step(self, ob, rew, done):
prev_frames, (_, _, prev_w), _, _ = self.prev_ob
frames, (row, col, w), _, _ = ob
if self.double_replay_buffer is not None:
self.double_replay_buffer.add(prev_frames, self.prev_ac, rew,
(row, col - w, w - prev_w), frames,
done)
self.optimize()
if not done:
ac = self.choose_action(ob)
else:
ac = None
self.add_to_renderer(ob)
self.t += 1
self.episode_rewards[-1] += rew
self.prev_ob = ob
self.prev_ac = ac
return ac
def choose_action(self, ob):
frames, (row, col, w), screen, (full_r, full_c) = ob
q_map_values = None
q_map_candidates = []
q_map_biased_candidates = []
# render Q-maps all the time even if we do not need them
if self.use_q_map:
q_map_values = self.q_map.compute_q_values(
frames[None])[0] # (rows, cols, acs)
if self.np_random.rand() < self.random_proba or (
not self.use_dqn and self.t <= self.using_q_map_starts):
ac = self.np_random.randint(self.n_actions)
action_type = 'random'
else:
# Q-Map available and started to train
if self.use_q_map and self.t > self.using_q_map_starts:
# reached goal
if self.q_map_goal_timer > 0 and self.q_map_goal[1] < w:
self.q_map_goal_timer = 0
self.q_map_goal = None
# goal unreachable
if self.q_map_goal_timer > 0 and (row, col) == self.q_map_goal:
self.q_map_goal_timer = 0
self.q_map_goal = None
# no more goal
if self.q_map_goal_timer == 0:
if self.np_random.rand() < self.q_map_goal_proba:
# find a new goal
q_map_max_values = q_map_values.max(2) # (rows, cols)
q_map_candidates_mask = np.logical_and(
self.q_map_min_q_value <= q_map_max_values,
self.q_map_max_q_value >= q_map_max_values)
q_map_candidates = np.where(q_map_candidates_mask)
q_map_candidates = np.dstack(q_map_candidates)[
0] # list of (row, col)
if len(q_map_candidates) > 0:
# goals compatible with greedy action
if self.use_dqn and self.np_random.rand(
) < self.q_map_greedy_bias:
greedy_ac = self.dqn.choose_action(
frames, stochastic=False)
q_map_biased_candidates_mask = np.logical_and(
q_map_candidates_mask,
q_map_values.argmax(2) == greedy_ac)
q_map_biased_candidates = np.where(
q_map_biased_candidates_mask)
q_map_biased_candidates = np.dstack(
q_map_biased_candidates)[
0] # list of (row, col)
# same DQN and Q-Map action
if len(q_map_biased_candidates) > 0:
goal_idx = self.np_random.randint(
len(q_map_biased_candidates))
q_map_goal_row, q_map_goal_col_local = q_map_biased_candidates[
goal_idx]
q_map_expected_steps = math.log(
q_map_max_values[q_map_goal_row,
q_map_goal_col_local],
self.q_map_gamma) + 1
self.q_map_goal_timer = math.ceil(
1.5 * q_map_expected_steps) # 50% bonus
self.q_map_goal = (q_map_goal_row,
q_map_goal_col_local + w)
ac = greedy_ac
action_type = 'dqn/qmap'
# greedy Q-Map action
else:
goal_idx = self.np_random.randint(
len(q_map_candidates))
q_map_goal_row, q_map_goal_col_local = q_map_candidates[
goal_idx]
q_map_expected_steps = math.log(
q_map_max_values[q_map_goal_row,
q_map_goal_col_local],
self.q_map_gamma) + 1
self.q_map_goal_timer = math.ceil(
(1. + self.q_map_timer_bonus) *
q_map_expected_steps)
self.q_map_goal = (q_map_goal_row,
q_map_goal_col_local + w)
ac, q_map_values = self.q_map.choose_action(
None,
(q_map_goal_row, q_map_goal_col_local),
q_map_values
) # no need to recompute the Q-Map
action_type = 'qmap'
self.q_map_goal_timer -= 1
if self.q_map_goal_timer == 0:
self.q_map_goal = None
# random action
else:
self.q_map_goal_timer = 0
self.q_map_goal = None
ac = self.np_random.randint(self.n_actions)
action_type = 'random'
# DQN action
else:
ac = self.dqn.choose_action(frames, stochastic=False)
action_type = 'dqn'
# Q-Map action
else:
q_map_goal_row, q_map_goal_col = self.q_map_goal
q_map_goal_col_local = q_map_goal_col - w
ac, q_map_values = self.q_map.choose_action(
frames, (q_map_goal_row, q_map_goal_col_local))
self.q_map_goal_timer -= 1
if self.q_map_goal_timer == 0:
self.q_map_goal = None
action_type = 'qmap'
# DQN action
else:
ac = self.dqn.choose_action(frames, stochastic=False)
action_type = 'dqn'
# rendering
self.add_to_renderer(ob, q_map_values, ac, action_type,
q_map_candidates, q_map_biased_candidates)
# update exploration
if action_type == 'dqn/qmap':
self.random_freq += 0.01 * (0 - self.random_freq)
self.greedy_freq += 0.01 * (1 - self.greedy_freq)
self.goal_freq += 0.01 * (0 - self.goal_freq) # TODO: 1?
elif action_type == 'dqn':
self.random_freq += 0.01 * (0 - self.random_freq)
self.greedy_freq += 0.01 * (1 - self.greedy_freq)
self.goal_freq += 0.01 * (0 - self.goal_freq)
elif action_type == 'qmap':
self.random_freq += 0.01 * (0 - self.random_freq)
self.greedy_freq += 0.01 * (0 - self.greedy_freq)
self.goal_freq += 0.01 * (1 - self.goal_freq)
elif action_type == 'random':
self.random_freq += 0.01 * (1 - self.random_freq)
self.greedy_freq += 0.01 * (0 - self.greedy_freq)
self.goal_freq += 0.01 * (0 - self.goal_freq)
else:
raise NotImplementedError(
'unknown action type {}'.format(action_type))
target_exploration = self.exploration_schedule.value(self.t)
current_exploration = (1.0 - self.greedy_freq)
if self.use_q_map and self.t >= self.using_q_map_starts:
self.random_proba = self.q_map_random_schedule.value(self.t)
if current_exploration > target_exploration:
self.q_map_goal_proba -= 0.001
elif current_exploration < target_exploration:
self.q_map_goal_proba += 0.001
else:
self.random_proba = self.exploration_schedule.value(self.t)
if (self.t + 1) % 100 == 0:
self.exploration_logger.log(self.t + 1, target_exploration,
current_exploration, self.random_freq,
self.goal_freq, self.greedy_freq)
return ac
def optimize(self):
if (
self.use_dqn or self.use_q_map
) and self.t >= self.learning_starts and self.t % self.train_freq == 0:
if self.use_dqn:
self.dqn.optimize(self.t)
if self.use_q_map:
self.q_map.optimize(self.t)
if self.use_dqn and self.t >= self.learning_starts and self.t % self.dqn_target_net_update_freq == 0:
self.dqn.update_target()
if self.use_q_map and self.t >= self.learning_starts and self.t % self.q_map_target_net_update_freq == 0:
self.q_map.update_target()
# save the session
if (self.use_dqn or self.use_q_map) and (self.t + 1) % 100000 == 0:
file_name = self.tensorflow_path + '/step_' + str(self.t +
1) + '.ckpt'
print('saving tensorflow session to', file_name)
self.tf_saver.save(tf.get_default_session(), file_name)
def log(self):
if self.t > 0 and self.print_freq is not None and len(
self.episode_rewards) % self.print_freq == 0:
mean_100ep_reward = np.mean(self.episode_rewards[-100:])
num_episodes = len(self.episode_rewards)
logger.record_tabular('steps', self.t)
logger.record_tabular('episodes', num_episodes)
logger.record_tabular('mean 100 episode reward',
'{:.3f}'.format(mean_100ep_reward))
logger.record_tabular(
'exploration (target)', '{:.3f} %'.format(
100 * self.exploration_schedule.value(self.t)))
logger.record_tabular(
'exploration (current)',
'{:.3f} %'.format(100 * (1.0 - self.greedy_freq)))
logger.dump_tabular()
def load(self, path):
self.tf_saver.restore(tf.get_default_session(), path)
print('model restored :)')
def add_to_renderer(self,
ob,
q_map_values=None,
ac=None,
action_type='',
q_map_candidates=[],
q_map_biased_candidates=[]):
if self.renderer is not None:
if self.use_q_map and self.q_map_goal is not None:
goal = self.q_map_goal
assert self.q_map_goal_timer > 0
else:
goal = None
self.renderer.add(ob, self.coords_shape, q_map_values, ac,
action_type, self.n_actions, q_map_candidates,
q_map_biased_candidates, goal)
