class BaseTrainer(object):
def __init__(self,
runner,
global_network,
initial_learning_rate,
learning_rate_input,
grad_applier,
env_type,
env_name,
entropy_beta,
gamma,
experience,
max_global_time_step,
device,
value_lambda):
self.runner = runner
self.learning_rate_input = learning_rate_input
self.env_type = env_type
self.env_name = env_name
self.gamma = gamma
self.max_global_time_step = max_global_time_step
self.action_size = Environment.get_action_size(env_type, env_name)
self.obs_size = Environment.get_obs_size(env_type, env_name)
self.global_network = global_network
self.local_network = UnrealModel(self.action_size,
self.obs_size,
1,
entropy_beta,
device,
value_lambda=value_lambda)
self.local_network.prepare_loss()
self.apply_gradients = grad_applier.minimize_local(self.local_network.total_loss,
self.global_network.get_vars(),
self.local_network.get_vars())
self.sync = self.local_network.sync_from(self.global_network, name="base_trainer")
self.experience = experience
self.local_t = 0
self.next_log_t = 0
self.next_performance_t = PERFORMANCE_LOG_INTERVAL
self.initial_learning_rate = initial_learning_rate
self.episode_reward = 0
# trackers for the experience replay creation
self.last_state = None
self.last_action = 0
self.last_reward = 0
self.ep_ploss = 0.
self.ep_vloss = 0.
self.ep_entr = []
self.ep_grad = []
self.ep_l = 0
def _anneal_learning_rate(self, global_time_step):
learning_rate = self.initial_learning_rate * (self.max_global_time_step - global_time_step) / self.max_global_time_step
if learning_rate < 0.0:
learning_rate = 0.0
return learning_rate
def choose_action(self, pi_values):
return np.random.choice(range(len(pi_values)), p=pi_values)
def set_start_time(self, start_time, global_t):
self.start_time = start_time
self.local_t = global_t
def pull_batch_from_queue(self):
"""
take a rollout from the queue of the thread runner.
"""
rollout_full = False
count = 0
while not rollout_full:
if count == 0:
rollout = self.runner.queue.get(timeout=600.0)
count += 1
else:
try:
rollout.extend(self.runner.queue.get_nowait())
count += 1
except queue.Empty:
#logger.warn("!!! queue was empty !!!")
continue
if count == 5 or rollout.terminal:
rollout_full = True
#logger.debug("pulled batch from rollout, length:{}".format(len(rollout.rewards)))
return rollout
def _print_log(self, global_t):
elapsed_time = time.time() - self.start_time
steps_per_sec = global_t / elapsed_time
logger.info("Performance : {} STEPS in {:.0f} sec. {:.0f} STEPS/sec. {:.2f}M STEPS/hour".format(
global_t, elapsed_time, steps_per_sec, steps_per_sec * 3600 / 1000000.))
def _add_batch_to_exp(self, batch):
# if we just started, copy the first state as last state
if self.last_state is None:
self.last_state = batch.si[0]
#logger.debug("adding batch to exp. len:{}".format(len(batch.si)))
for k in range(len(batch.si)):
state = batch.si[k]
action = batch.a[k]#np.argmax(batch.a[k])
reward = batch.a_r[k][-1]
self.episode_reward += reward
features = batch.features[k]
pixel_change = batch.pc[k]
#logger.debug("k = {} of {} -- terminal = {}".format(k,len(batch.si), batch.terminal))
if k == len(batch.si)-1 and batch.terminal:
terminal = True
else:
terminal = False
frame = ExperienceFrame(state, reward, action, terminal, features, pixel_change,
self.last_action, self.last_reward)
self.experience.add_frame(frame)
self.last_state = state
self.last_action = action
self.last_reward = reward
if terminal:
total_ep_reward = self.episode_reward
self.episode_reward = 0
return total_ep_reward
else:
return None
def process(self, sess, global_t, summary_writer, summary_op, summary_values, base_lambda):
sess.run(self.sync)
cur_learning_rate = self._anneal_learning_rate(global_t)
# Copy weights from shared to local
#logger.debug("Syncing to global net -- current learning rate:{}".format(cur_learning_rate))
#logger.debug("local_t:{} - global_t:{}".format(self.local_t,global_t))
# get batch from process_rollout
rollout = self.pull_batch_from_queue()
batch = process_rollout(rollout, gamma=0.99, lambda_=base_lambda)
self.local_t += len(batch.si)
#logger.debug("si:{}".format(batch.si.shape))
feed_dict = {
self.local_network.base_input: batch.si,
self.local_network.base_last_action_reward_input: batch.a_r,
self.local_network.base_a: batch.a,
self.local_network.base_adv: batch.adv,
self.local_network.base_r: batch.r,
#self.local_network.base_initial_lstm_state: batch.features[0],
# [common]
self.learning_rate_input: cur_learning_rate
}
# Calculate gradients and copy them to global network.
[_, grad], policy_loss, value_loss, entropy, baseinput, policy, value = sess.run(
[self.apply_gradients,
self.local_network.policy_loss,
self.local_network.value_loss,
self.local_network.entropy,
self.local_network.base_input,
self.local_network.base_pi,
self.local_network.base_v],
feed_dict=feed_dict )
self.ep_l += batch.si.shape[0]
self.ep_ploss += policy_loss
self.ep_vloss += value_loss
self.ep_entr.append(entropy)
self.ep_grad.append(grad)
# add batch to experience replay
total_ep_reward = self._add_batch_to_exp(batch)
if total_ep_reward is not None:
laststate = baseinput[np.newaxis,-1,...]
summary_str = sess.run(summary_op, feed_dict={summary_values[0]: total_ep_reward,
summary_values[1]: self.ep_l,
summary_values[2]: self.ep_ploss/self.ep_l,
summary_values[3]: self.ep_vloss/self.ep_l,
summary_values[4]: np.mean(self.ep_entr),
summary_values[5]: np.mean(self.ep_grad),
summary_values[6]: cur_learning_rate})#,
#summary_values[7]: laststate})
summary_writer.add_summary(summary_str, global_t)
summary_writer.flush()
if self.local_t > self.next_performance_t:
self._print_log(global_t)
self.next_performance_t += PERFORMANCE_LOG_INTERVAL
if self.local_t >= self.next_log_t:
logger.info("localtime={}".format(self.local_t))
logger.info("action={}".format(self.last_action))
logger.info("policy={}".format(policy[-1]))
logger.info("V={}".format(np.mean(value)))
logger.info("ep score={}".format(total_ep_reward))
self.next_log_t += LOG_INTERVAL
#try:
#sess.run(self.sync)
#except Exception:
# logger.warn("--- !! parallel syncing !! ---")
self.ep_l = 0
self.ep_ploss = 0.
self.ep_vloss = 0.
self.ep_entr = []
self.ep_grad = []
# Return advanced local step size
diff_global_t = self.local_t - global_t
return diff_global_t
class AuxTrainer(object):
def __init__(self, global_network, thread_index, use_base,
use_pixel_change, use_value_replay, use_reward_prediction,
use_temporal_coherence, use_proportionality, use_causality,
use_repeatability, value_lambda, pixel_change_lambda,
temporal_coherence_lambda, proportionality_lambda,
causality_lambda, repeatability_lambda, initial_learning_rate,
learning_rate_input, grad_applier, aux_t, env_type, env_name,
entropy_beta, local_t_max, gamma, aux_lambda, gamma_pc,
experience, max_global_time_step, device):
self.use_pixel_change = use_pixel_change
self.use_value_replay = use_value_replay
self.use_reward_prediction = use_reward_prediction
self.use_temporal_coherence = use_temporal_coherence
self.use_proportionality = use_proportionality
self.use_causality = use_causality
self.use_repeatability = use_repeatability
self.learning_rate_input = learning_rate_input
self.env_type = env_type
self.env_name = env_name
self.entropy_beta = entropy_beta
self.local_t = 0
self.next_sync_t = 0
self.next_log_t = 0
self.local_t_max = local_t_max
self.gamma = gamma
self.aux_lambda = aux_lambda
self.gamma_pc = gamma_pc
self.experience = experience
self.max_global_time_step = max_global_time_step
self.action_size = Environment.get_action_size(env_type, env_name)
self.obs_size = Environment.get_obs_size(env_type, env_name)
self.thread_index = thread_index
self.local_network = UnrealModel(
self.action_size,
self.obs_size,
self.thread_index,
self.entropy_beta,
device,
use_pixel_change=use_pixel_change,
use_value_replay=use_value_replay,
use_reward_prediction=use_reward_prediction,
use_temporal_coherence=use_temporal_coherence,
use_proportionality=use_proportionality,
use_causality=use_causality,
use_repeatability=use_repeatability,
value_lambda=value_lambda,
pixel_change_lambda=pixel_change_lambda,
temporal_coherence_lambda=temporal_coherence_lambda,
proportionality_lambda=proportionality_lambda,
causality_lambda=causality_lambda,
repeatability_lambda=repeatability_lambda,
for_display=False,
use_base=use_base)
self.local_network.prepare_loss()
self.global_network = global_network
#logger.debug("ln.total_loss:{}".format(self.local_network.total_loss))
self.apply_gradients = grad_applier.minimize_local(
self.local_network.total_loss, self.global_network.get_vars(),
self.local_network.get_vars())
self.sync = self.local_network.sync_from(self.global_network,
name="aux_trainer_{}".format(
self.thread_index))
self.initial_learning_rate = initial_learning_rate
self.episode_reward = 0
# trackers for the experience replay creation
self.last_action = np.zeros(self.action_size)
self.last_reward = 0
self.aux_losses = []
self.aux_losses.append(self.local_network.policy_loss)
self.aux_losses.append(self.local_network.value_loss)
if self.use_pixel_change:
self.aux_losses.append(self.local_network.pc_loss)
if self.use_value_replay:
self.aux_losses.append(self.local_network.vr_loss)
if self.use_reward_prediction:
self.aux_losses.append(self.local_network.rp_loss)
if self.use_temporal_coherence:
self.aux_losses.append(self.local_network.tc_loss)
if self.use_proportionality:
self.aux_losses.append(self.local_network.prop_loss)
if self.use_causality:
self.aux_losses.append(self.local_network.caus_loss)
if self.use_repeatability:
self.aux_losses.append(self.local_network.rep_loss)
def _anneal_learning_rate(self, global_time_step):
learning_rate = self.initial_learning_rate * (
self.max_global_time_step -
global_time_step) / self.max_global_time_step
if learning_rate < 0.0:
learning_rate = 0.0
return learning_rate
def discount(self, x, gamma):
return scipy.signal.lfilter([1], [1, -gamma], x[::-1], axis=0)[::-1]
def _process_base(self, sess, policy, gamma, lambda_=1.0):
# base A3C from experience replay
experience_frames = self.experience.sample_sequence(self.local_t_max +
1)
batch_si = []
batch_a = []
rewards = []
action_reward = []
batch_features = []
values = []
last_state = experience_frames[0].state
last_action_reward = experience_frames[0].concat_action_and_reward(
experience_frames[0].action, self.action_size,
experience_frames[0].reward)
policy.set_state(
np.asarray(experience_frames[0].features).reshape([2, 1, -1]))
for frame in range(1, len(experience_frames)):
state = experience_frames[frame].state
#logger.debug("state:{}".format(state.shape))
batch_si.append(state)
action = experience_frames[frame].action
reward = experience_frames[frame].reward
a_r = experience_frames[frame].concat_action_and_reward(
action, self.action_size, reward)
action_reward.append(a_r)
batch_a.append(a_r[:-1])
rewards.append(reward)
_, value, features = policy.run_base_policy_and_value(
sess, last_state, last_action_reward)
batch_features.append(features)
values.append(value)
last_state = state
last_action_reward = action_reward[-1]
if not experience_frames[-1].terminal:
r = policy.run_base_value(sess, last_state, last_action_reward)
else:
r = 0.
vpred_t = np.asarray(values + [r])
rewards_plus_v = np.asarray(rewards + [r])
batch_r = self.discount(rewards_plus_v, gamma)[:-1]
delta_t = rewards + gamma * vpred_t[1:] - vpred_t[:-1]
# this formula for the advantage comes "Generalized Advantage Estimation":
# https://arxiv.org/abs/1506.02438
batch_adv = self.discount(delta_t, gamma * lambda_)
start_features = [] #batch_features[0]
return Batch(batch_si, batch_a, action_reward, batch_adv, batch_r,
experience_frames[-1].terminal, start_features)
def _process_pc(self, sess):
# [pixel change]
# Sample 20+1 frame (+1 for last next state)
pc_experience_frames = self.experience.sample_sequence(
self.local_t_max + 1)
# Revese sequence to calculate from the last
pc_experience_frames.reverse()
batch_pc_si = []
batch_pc_a = []
batch_pc_R = []
batch_pc_last_action_reward = []
pc_R = np.zeros([20, 20], dtype=np.float32)
if not pc_experience_frames[0].terminal:
pc_R = self.local_network.run_pc_q_max(
sess, pc_experience_frames[0].state,
pc_experience_frames[0].get_last_action_reward(
self.action_size))
for frame in pc_experience_frames[1:]:
pc_R = frame.pixel_change + self.gamma_pc * pc_R
a = np.zeros([self.action_size])
a[frame.action] = 1.0
last_action_reward = frame.get_last_action_reward(self.action_size)
batch_pc_si.append(frame.state)
batch_pc_a.append(a)
batch_pc_R.append(pc_R)
batch_pc_last_action_reward.append(last_action_reward)
batch_pc_si.reverse()
batch_pc_a.reverse()
batch_pc_R.reverse()
batch_pc_last_action_reward.reverse()
return batch_pc_si, batch_pc_last_action_reward, batch_pc_a, batch_pc_R
def _process_vr(self, sess):
# [Value replay]
# Sample 20+1 frame (+1 for last next state)
vr_experience_frames = self.experience.sample_sequence(
self.local_t_max + 1)
# Revese sequence to calculate from the last
vr_experience_frames.reverse()
batch_vr_si = []
batch_vr_R = []
batch_vr_last_action_reward = []
vr_R = 0.0
if not vr_experience_frames[0].terminal:
vr_R = self.local_network.run_vr_value(
sess, vr_experience_frames[0].state,
vr_experience_frames[0].get_last_action_reward(
self.action_size))
# t_max times loop
for frame in vr_experience_frames[1:]:
vr_R = frame.reward + self.gamma * vr_R
batch_vr_si.append(frame.state)
batch_vr_R.append(vr_R)
last_action_reward = frame.get_last_action_reward(self.action_size)
batch_vr_last_action_reward.append(last_action_reward)
batch_vr_si.reverse()
batch_vr_R.reverse()
batch_vr_last_action_reward.reverse()
return batch_vr_si, batch_vr_last_action_reward, batch_vr_R
def _process_rp(self):
# [Reward prediction]
rp_experience_frames = self.experience.sample_rp_sequence()
# 4 frames
batch_rp_si = []
batch_rp_c = []
for i in range(3):
batch_rp_si.append(rp_experience_frames[i].state)
# one hot vector for target reward
r = rp_experience_frames[3].reward
rp_c = [0.0, 0.0, 0.0]
if r == 0:
rp_c[0] = 1.0 # zero
elif r > 0:
rp_c[1] = 1.0 # positive
else:
rp_c[2] = 1.0 # negative
batch_rp_c.append(rp_c)
return batch_rp_si, batch_rp_c
def _process_robotics(self):
# [proportionality, causality and repeatability]
frames1, frames2 = self.experience.sample_b2b_seq_recursive(
self.local_t_max + 1)
b_inp1_1 = []
b_inp1_2 = []
b_inp2_1 = []
b_inp2_2 = []
actioncheck = []
rewardcheck = []
for frame in range(len(frames1) - 1):
b_inp1_1.append(frames1[frame].state)
b_inp1_2.append(frames1[frame + 1].state)
b_inp2_1.append(frames2[frame].state)
b_inp2_2.append(frames2[frame + 1].state)
if np.argmax(frames1[frame].action) == np.argmax(
frames2[frame].action):
actioncheck.append(1)
else:
actioncheck.append(0)
if frames1[frame].reward == frames2[frame].reward:
rewardcheck.append(0)
else:
rewardcheck.append(1)
#logger.debug(actioncheck)
return b_inp1_1, b_inp1_2, b_inp2_1, b_inp2_2, actioncheck, rewardcheck
def process(self, sess, global_t, aux_t, summary_writer, summary_op_aux,
summary_aux):
sess.run(self.sync)
cur_learning_rate = self._anneal_learning_rate(global_t)
"""
if self.local_t >= self.next_sync_t:
# Copy weights from shared to local
#logger.debug("aux_t:{} -- local_t:{} -- syncing...".format(aux_t, self.local_t))
try:
sess.run(self.sync)
self.next_sync_t += SYNC_INTERVAL
except Exception:
logger.warn("--- !! parallel syncing !! ---")
#logger.debug("next_sync:{}".format(self.next_sync_t))
"""
batch = self._process_base(sess, self.local_network, self.gamma,
self.aux_lambda)
feed_dict = {
self.local_network.base_input:
batch.si,
self.local_network.base_last_action_reward_input:
batch.a_r,
self.local_network.base_a:
batch.a,
self.local_network.base_adv:
batch.adv,
self.local_network.base_r:
batch.r,
#self.local_network.base_initial_lstm_state: batch.features,
# [common]
self.learning_rate_input:
cur_learning_rate
}
# [Pixel change]
if self.use_pixel_change:
batch_pc_si, batch_pc_last_action_reward, batch_pc_a, batch_pc_R = self._process_pc(
sess)
pc_feed_dict = {
self.local_network.pc_input: batch_pc_si,
self.local_network.pc_last_action_reward_input:
batch_pc_last_action_reward,
self.local_network.pc_a: batch_pc_a,
self.local_network.pc_r: batch_pc_R,
# [common]
self.learning_rate_input: cur_learning_rate
}
feed_dict.update(pc_feed_dict)
# [Value replay]
if self.use_value_replay:
batch_vr_si, batch_vr_last_action_reward, batch_vr_R = self._process_vr(
sess)
vr_feed_dict = {
self.local_network.vr_input: batch_vr_si,
self.local_network.vr_last_action_reward_input:
batch_vr_last_action_reward,
self.local_network.vr_r: batch_vr_R,
# [common]
self.learning_rate_input: cur_learning_rate
}
feed_dict.update(vr_feed_dict)
# [Reward prediction]
if self.use_reward_prediction:
batch_rp_si, batch_rp_c = self._process_rp()
rp_feed_dict = {
self.local_network.rp_input: batch_rp_si,
self.local_network.rp_c_target: batch_rp_c,
# [common]
self.learning_rate_input: cur_learning_rate
}
feed_dict.update(rp_feed_dict)
# [Robotic Priors]
if self.use_temporal_coherence or self.use_proportionality or self.use_causality or self.use_repeatability:
bri11, bri12, bri21, bri22, sameact, diffrew = self._process_robotics(
)
#logger.debug("sameact:{}".format(sameact))
#logger.debug("diffrew:{}".format(diffrew))
if self.use_temporal_coherence:
tc_feed_dict = {
self.local_network.tc_input1: np.asarray(bri11),
self.local_network.tc_input2: np.asarray(bri12)
}
feed_dict.update(tc_feed_dict)
if self.use_proportionality:
prop_feed_dict = {
self.local_network.prop_input1_1: np.asarray(bri11),
self.local_network.prop_input1_2: np.asarray(bri12),
self.local_network.prop_input2_1: np.asarray(bri21),
self.local_network.prop_input2_2: np.asarray(bri22),
self.local_network.prop_actioncheck: np.asarray(sameact)
}
feed_dict.update(prop_feed_dict)
if self.use_causality:
caus_feed_dict = {
self.local_network.caus_input1: np.asarray(bri11),
self.local_network.caus_input2: np.asarray(bri21),
self.local_network.caus_actioncheck: np.asarray(sameact),
self.local_network.caus_rewardcheck: np.asarray(diffrew)
}
feed_dict.update(caus_feed_dict)
if self.use_repeatability:
rep_feed_dict = {
self.local_network.rep_input1_1: np.asarray(bri11),
self.local_network.rep_input1_2: np.asarray(bri12),
self.local_network.rep_input2_1: np.asarray(bri21),
self.local_network.rep_input2_2: np.asarray(bri22),
self.local_network.rep_actioncheck: np.asarray(sameact)
}
feed_dict.update(rep_feed_dict)
# Calculate gradients and copy them to global netowrk.
[_, grad], losses, entropy = sess.run([
self.apply_gradients, self.aux_losses, self.local_network.entropy
],
feed_dict=feed_dict)
if self.thread_index == 2 and aux_t >= self.next_log_t:
#logger.debug("losses:{}".format(losses))
self.next_log_t += LOG_INTERVAL
feed_dict_aux = {}
for k in range(len(losses)):
feed_dict_aux.update({summary_aux[k]: losses[k]})
feed_dict_aux.update({
summary_aux[-2]: np.mean(entropy),
summary_aux[-1]: np.mean(grad)
})
summary_str = sess.run(summary_op_aux, feed_dict=feed_dict_aux)
summary_writer.add_summary(summary_str, aux_t)
summary_writer.flush()
self.local_t += len(batch.si)
return len(batch.si)