class A3CTrainingThread(object):
def __init__(self,
thread_index,
global_network,
initial_learning_rate,
learning_rate_input,
grad_applier,
max_global_time_step,
device,
network_scope="network",
scene_scope="scene",
task_scope="task"):
self.thread_index = thread_index
self.learning_rate_input = learning_rate_input
self.max_global_time_step = max_global_time_step
self.network_scope = network_scope
self.scene_scope = scene_scope
self.task_scope = task_scope
self.scopes = [network_scope, scene_scope, task_scope]
self.local_network = ActorCriticFFNetwork(action_size=ACTION_SIZE,
device=device,
network_scope=network_scope,
scene_scopes=[scene_scope])
self.local_network.prepare_loss(ENTROPY_BETA, self.scopes)
self.trainer = AccumTrainer(device)
self.trainer.prepare_minimize(self.local_network.total_loss,
self.local_network.get_vars())
self.accum_gradients = self.trainer.accumulate_gradients()
self.reset_gradients = self.trainer.reset_gradients()
accum_grad_names = [
self._local_var_name(x)
for x in self.trainer.get_accum_grad_list()
]
global_net_vars = [
x for x in global_network.get_vars()
if self._get_accum_grad_name(x) in accum_grad_names
]
self.apply_gradients = grad_applier.apply_gradients(
global_net_vars, self.trainer.get_accum_grad_list())
self.sync = self.local_network.sync_from(global_network)
self.env = None
self.local_t = 0
self.initial_learning_rate = initial_learning_rate
self.episode_reward = 0
self.episode_length = 0
self.episode_max_q = -np.inf
def _local_var_name(self, var):
return '/'.join(var.name.split('/')[1:])
def _get_accum_grad_name(self, var):
return self._local_var_name(var).replace(':', '_') + '_accum_grad:0'
def _anneal_learning_rate(self, global_time_step):
time_step_to_go = max(self.max_global_time_step - global_time_step,
0.0)
learning_rate = self.initial_learning_rate * time_step_to_go / self.max_global_time_step
return learning_rate
def choose_action(self, pi_values):
values = []
sum = 0.0
for rate in pi_values:
sum = sum + rate
value = sum
values.append(value)
r = random.random() * sum
for i in range(len(values)):
if values[i] >= r:
return i
# fail safe
return len(values) - 1
def _record_score(self, sess, writer, summary_op, placeholders, values,
global_t):
feed_dict = {}
for k in placeholders:
feed_dict[placeholders[k]] = values[k]
summary_str = sess.run(summary_op, feed_dict=feed_dict)
if VERBOSE:
sys.stdout.write('writing to summary writer at time %d\n' %
(global_t))
writer.add_summary(summary_str, global_t)
# writer.flush()
def process(self, sess, global_t, summary_writer, summary_op,
summary_placeholders):
if self.env is None:
# lazy evaluation
time.sleep(self.thread_index * 1.0)
self.env = Environment({
'scene_name': self.scene_scope,
'terminal_state_id': int(self.task_scope)
})
states = []
actions = []
rewards = []
values = []
targets = []
rnn_inits = []
state_representation = []
usf = []
reward_vector = []
terminal_end = False
# reset accumulated gradients
sess.run(self.reset_gradients)
# copy weights from shared to local
sess.run(self.sync)
#At each episode start we set the initial state of the RNN to zero
start_local_t = self.local_t
start_lstm_state = self.local_network.lstm_state_out
# t_max times loop
for i in range(LOCAL_T_MAX):
pi_, value_, usf_s_g = self.local_network.run_policy_and_value(
sess, self.env.s_t, self.env.target, self.scopes)
imidia_s = self.local_network.run_state(sess, self.env.s_t,
self.scopes)
#usf_s_g = self.local_network.run_usf(sess, self.env.s_t, self.env.target,self.rnn_state_init[0] ,self.rnn_state_init[1] ,self.scopes)
action = self.choose_action(pi_)
states.append(self.env.s_t)
actions.append(action)
values.append(value_)
targets.append(self.env.target)
usf.append(usf_s_g)
state_representation.append(imidia_s)
if VERBOSE and (self.thread_index
== 0) and (self.local_t % 1000) == 0:
sys.stdout.write("Pi = {0} V = {1}\n".format(pi_, value_))
# process game
self.env.step(action)
# receive game result
reward = self.env.reward
terminal = self.env.terminal
# ad-hoc reward for navigation
reward = 10.0 if terminal else -0.01
if self.episode_length > 5e3: terminal = True
self.episode_reward += reward
self.episode_length += 1
self.episode_max_q = max(self.episode_max_q, np.max(value_))
# clip reward
rewards.append(np.clip(reward, -1, 1))
self.local_t += 1
# s_t1 -> s_t
self.env.update()
if i == (LOCAL_T_MAX - 1) or terminal:
imidiate_state_representation_next = []
usf_next = []
#reward_vector_predictor_next=[]
last_state = self.env.s_t
imidia_s_next = self.local_network.run_state(
sess, self.env.s_t, self.scopes)
state_representation_next = state_representation[1:] + [
imidia_s_next
]
if terminal:
usf_next_imi = 0
else:
usf_next_imi = self.local_network.run_usf(
sess, self.env.s_t, self.env.target, self.scopes)
usf_next = usf[1:] + [usf_next_imi]
if terminal:
terminal_end = True
sys.stdout.write(
"time %d | thread #%d | scene %s | target #%s\n%s %s episode reward = %.3f\n%s %s episode length = %d\n%s %s episode max Q = %.3f\n"
% (global_t, self.thread_index, self.scene_scope,
self.task_scope, self.scene_scope, self.task_scope,
self.episode_reward, self.scene_scope, self.task_scope,
self.episode_length, self.scene_scope, self.task_scope,
self.episode_max_q))
oneResult = [
global_t, self.thread_index, self.scene_scope,
self.task_scope, self.episode_reward, self.episode_length,
self.episode_max_q
]
with open('trainingOutput.csv', 'a+') as fp:
# fd.write(oneResult)
wr = csv.writer(fp)
wr.writerow(oneResult)
summary_values = {
"episode_reward_input": self.episode_reward,
"episode_length_input": float(self.episode_length),
"episode_max_q_input": self.episode_max_q,
"learning_rate_input": self._anneal_learning_rate(global_t)
}
self._record_score(sess, summary_writer, summary_op,
summary_placeholders, summary_values,
global_t)
self.episode_reward = 0
self.episode_length = 0
self.episode_max_q = -np.inf
self.local_network.reset_state()
self.env.reset()
break
R = 0.0
usf_R = 0.0
if not terminal_end:
R = self.local_network.run_value(sess, self.env.s_t,
self.env.target, self.scopes)
usf_R = self.local_network.run_usf(sess, self.env.s_t,
self.env.target, self.scopes)
actions.reverse()
states.reverse()
rewards.reverse()
values.reverse()
state_representation.reverse()
state_representation_next.reverse()
usf_next.reverse()
batch_si = []
batch_a = []
batch_td = []
batch_R = []
batch_usf_R = []
batch_t = []
# compute and accmulate gradients
for (ai, ri, si, Vi, ti, state,
usf_n) in zip(actions, rewards, states, values, targets,
state_representation_next, usf_next):
R = ri + GAMMA * R
usf_R = state + GAMMA * usf_R
#usf_R = state + GAMMA*usf_n
td = R - Vi
a = np.zeros([ACTION_SIZE])
a[ai] = 1
batch_si.append(si)
batch_a.append(a)
batch_td.append(td)
batch_R.append(R)
batch_usf_R.append(usf_R)
batch_t.append(ti)
#We need to reverse this since in the training we unroll for 5 steps unlike in the inferences
batch_si.reverse()
batch_a.reverse()
batch_td.reverse()
batch_R.reverse()
batch_usf_R.reverse()
batch_t.reverse()
sess.run(self.accum_gradients,
feed_dict={
self.local_network.s: batch_si,
self.local_network.a: batch_a,
self.local_network.t: batch_t,
self.local_network.td: batch_td,
self.local_network.r: batch_R,
self.local_network.return_usf: batch_usf_R,
self.local_network.initial_lstm_state: start_lstm_state,
self.local_network.step_size: [len(batch_a)],
})
cur_learning_rate = self._anneal_learning_rate(global_t)
sess.run(self.apply_gradients,
feed_dict={self.learning_rate_input: cur_learning_rate})
if VERBOSE and (self.thread_index == 0) and (self.local_t % 100) == 0:
sys.stdout.write("Local timestep %d\n" % self.local_t)
# return advanced local step size
diff_local_t = self.local_t - start_local_t
return diff_local_t
ep_rewards = []
ep_lengths = []
ep_collisions = []
scopes = [network_scope, scene_scope, task_scope]
for i_episode in range(NUM_EVAL_EPISODES):
env.reset()
terminal = False
ep_reward = 0
ep_collision = 0
ep_t = 0
while not terminal:
usf_s_g = global_network.run_usf(sess, env.s_t, env.target,
scopes)
pi_values = global_network.run_policy(
sess, env.s_t, env.target, usf_s_g, scopes)
action = sample_action(pi_values)
env.step(action)
env.update()
terminal = env.terminal
if ep_t == 500: break
if env.collided: ep_collision += 1
ep_reward += env.reward
ep_t += 1
ep_lengths.append(ep_t)
ep_rewards.append(ep_reward)
