def __init__(self,
config,
global_network,
thread_index,
network_scope="network",
scene_scope="scene",
task_scope="task"):
self.thread_index = thread_index
self.config = config
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 = global_network
self.env = Environment({
'scene_name': self.scene_scope,
'terminal_state_id': int(self.task_scope)
})
self.env.reset()
self.expert = Expert(self.env)
self.local_t = 0
self.episode_length = 0
self.first_iteration = True # first iteration of Dagger
# training dataset
self.states = []
self.actions = []
self.targets = []
def _evaluate(self, sess, list_of_tasks, num_episodes, max_steps,
success_cutoff):
scene_scopes = list_of_tasks.keys()
results = {}
for scene_scope in scene_scopes:
for task_scope in list_of_tasks[scene_scope]:
env = Environment({
'scene_name': scene_scope,
'terminal_state_id': int(task_scope)
})
ep_lengths = []
ep_collisions = []
oracle_lengths = []
ep_successes = []
scopes = [self.network_scope, scene_scope, task_scope]
for i_episode in range(num_episodes):
env.reset()
oracle_lengths.append(env.shortest_path_distances[
env.current_state_id][env.terminal_state_id])
terminal = False
ep_length = 0
ep_collision = 0
while not terminal:
pi_values = self.local_network.run_policy(
sess, env.s_t, env.target, scopes)
action = sample_action(pi_values)
env.step(action)
env.update()
terminal = env.terminal
if ep_length == max_steps: break
if env.collided: ep_collision += 1
ep_length += 1
ep_lengths.append(ep_length)
ep_collisions.append(ep_collision)
ep_successes.append(int(ep_length < success_cutoff))
results[scene_scope + task_scope] = [
np.mean(ep_lengths),
np.mean(ep_collisions),
np.mean(oracle_lengths),
np.mean(ep_successes)
]
return results
def main(args):
self.scene_scope=bathroom_02
self.task_scope=37 #26 43 53 32 41
self.env = Environment({'scene_name': self.scene_scope,'terminal_state_id': int(self.task_scope)})
self.env.reset()
Policy = Policy_net('policy', env) #buiding the actor critic graph / object
PPO = PPOTrain(Policy, Old_Policy, gamma=args.gamma) #gradiet updatror object or the graph
pdb.set_trace()
D = Discriminator(env) #discriminator of the Gan Kind of thing
f = open("/home/hmi/Desktop/visual-navigation-checkpoint/path.py", "w")
#fo = open("/home/hmi/Desktop/visual-navigation-checkpoint/Dang_result.py", "a")
counter = 0
for scene_scope in scene_scopes:
scene_stats[scene_scope] = []
f.write('PATH = {')
#fo.write(scene_scope +'\n')
for task_scope in list_of_tasks[scene_scope]:
#fo.write('%s \n', %task_scope)
env = Environment({
'scene_name': scene_scope,
'terminal_state_id': task_scope[0],
'checkpoint_state_id': task_scope[1]
})
ep_rewards = []
ep_lengths = []
ep_collisions = []
scopes = [network_scope, scene_scope, task_scope]
print('evaluation: %s %s' % (scene_scope, task_scope))
viewer = SimpleImageViewer()
#NUM_EVAL_EPISODES
for i_episode in range(2):
env.reset()
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.training_scene,
'terminal_state_id': self.task_scope,
'checkpoint_state_id': self.checkpoint_scope
})
self.env.reset()
states = []
actions = []
rewards = []
values = []
targets = []
checkpoints = []
positions = []
auxilaries = []
auxilaries_cl = []
aclists = []
colists = []
isCheckpointed = []
collision = []
terminal_end = False
# reset accumulated gradients
sess.run(self.reset_gradients)
# copy weights from shared to local
sess.run(self.sync)
start_local_t = self.local_t
# t_max times loop
for i in range(LOCAL_T_MAX):
pi_, value_ = self.local_network.run_policy_and_value(
sess, self.env.s_t, self.env.s_position, self.env.checkpoint,
self.env.s_a_t, self.env.s_c_t, self.env.isCheckpoint,
self.env.s_aux_cl, self.scopes)
action = self.choose_action(pi_)
states.append(self.env.s_t)
actions.append(action)
values.append(value_)
targets.append(self.env.target)
checkpoints.append(self.env.checkpoint)
positions.append(self.env.s_position)
aclists.append(self.env.s_a_t)
colists.append(self.env.s_c_t)
collision.append(self.env.s_aux_cl)
isCheckpointed.append(int(self.env.isCheckpoint))
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
#if self.env.isCheckpoint:
# sys.stdout.write("CHECKPOINT \n")
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()
auxilaries.append(self.env.s_aux)
auxilaries_cl.append(self.env.s_aux_cl)
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))
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.env.reset()
break
R = 0.0
if not terminal_end:
R = self.local_network.run_value(sess, self.env.s_t,
self.env.s_position,
self.env.checkpoint,
self.env.s_a_t, self.env.s_c_t,
self.env.isCheckpoint,
self.env.s_aux_cl, self.scopes)
actions.reverse()
states.reverse()
rewards.reverse()
values.reverse()
positions.reverse()
auxilaries.reverse()
auxilaries_cl.reverse()
aclists.reverse()
colists.reverse()
isCheckpointed.reverse()
collision.reverse()
batch_si = []
batch_a = []
batch_td = []
batch_R = []
batch_t = []
batch_c = []
batch_p = []
batch_aux = []
batch_aux_cl = []
batch_al = []
batch_cl = []
batch_ic = []
batch_collision = []
# compute and accmulate gradients
for (ai, ri, si, Vi, ti, ci, pi, auxi, aux_cl_i, ali, cli, ici,
coli) in zip(actions, rewards, states, values, targets,
checkpoints, positions, auxilaries, auxilaries_cl,
aclists, colists, isCheckpointed, collision):
R = ri + GAMMA * R
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_t.append(ti)
batch_c.append(ci)
batch_p.append(pi)
batch_aux.append(auxi)
batch_aux_cl.append(aux_cl_i)
batch_al.append(ali)
batch_cl.append(cli)
batch_ic.append(ici)
batch_collision.append(coli)
sess.run(self.accum_gradients,
feed_dict={
self.local_network.s: batch_si,
self.local_network.a: batch_a,
self.local_network.t: batch_p,
self.local_network.c: batch_c,
self.local_network.td: batch_td,
self.local_network.aux: batch_aux,
self.local_network.aux_cl: batch_aux_cl,
self.local_network.al: batch_al,
self.local_network.cl: batch_cl,
self.local_network.ic: batch_ic,
self.local_network.col: batch_collision,
self.local_network.r: batch_R
})
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, terminal
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)
})
self.env.reset() #resetting the environment for each thread
self.env_Oracle = Environment(
{ #Every iteration in the thread the expert start with the current state of the agent
'scene_name': self.scene_scope,
'terminal_state_id': int(self.task_scope),
'initial_state': self.env.current_state_id
})
self.env_Oracle.reset()
states = [] #to keeep state ,actions ,targets and other stae
actions = []
rewards = []
values = []
targets = []
dones = []
states_oracle = []
actions_oracle = []
targets_oracle = []
terminal_end = False #in the start terminal state_end is false
sess.run(
self.reset_gradients
) #resetting the gradient positions when starting the process for each Iteration
sess.run(self.sync) # copy weights from shared to local
#dicriminator sync
##########################
sess.run(self.sync_discriminator_G_l
) #Copy the weights from the sharead to the local
sess.run(self.reset_gradients_d
) #resetting the gradients of the discriminator slosts
########################
start_local_t = self.local_t
self.oracle = ShortestPathOracle(self.env_Oracle, ACTION_SIZE)
#########################################################################################
#Sampling the Expert Trajectories
for i in range(100):
#We might need to use an for loop to finish the expert trajectory first
oracle_pi = self.oracle.run_policy(
self.env_Oracle.current_state_id
) #get the policy of the oracle which means the shotest path kind of action in the given state
oracle_action = self.choose_action(oracle_pi)
states_oracle.append(self.env_Oracle.s_t)
actions_oracle.append(oracle_action)
targets_oracle.append(self.env_Oracle.target)
self.env_Oracle.step(oracle_action)
terminal_o = self.env_Oracle.terminal
self.env_Oracle.update()
if terminal_o:
break
##############################################################################################
# t_max times loop
for i in range(
LOCAL_T_MAX
): #one thread will run for maximum amoound to 5 iterations then do a gradiet uodate
pi_, value_ = self.local_network.run_policy_and_value(
sess, self.env.s_t, self.env.target, self.scopes)
action = self.choose_action(pi_)
states.append(self.env.s_t)
actions.append(action)
values.append(value_)
targets.append(self.env.target)
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 #getting the reward from the env
terminal = self.env.terminal #geting whether the agent went to a terminal state
# ad-hoc reward for navigation
reward = 10.0 if terminal else -0.01 #this is the normal reward here 10 if terminal all the others it is -0.01 (ollision donesst take in to the accout)
if self.episode_length > 5e3:
terminal = True #Here we do not let agent to run more that 5000 steps so we make it terminal
#but the above terminal thing has no effect on giving 10 as the rwaerd because we set the rweard above
self.episode_reward += reward
self.episode_length += 1
#this is what is the maximum value got in the episode
self.episode_max_q = max(self.episode_max_q, np.max(
value_)) #self.episode_max_q-This is -inf in the beggining
# clip reward
rewards.append(
np.clip(reward, -1, 1)
) #make sure the rewartds is between -1 and +1 even thore rtthere is a 10
self.local_t += 1
# s_t1 -> s_t
self.env.update()
if terminal: #if we go to the terminal state we will surely break the function
score = self.local_discriminator.run_critic(
sess, states, targets, actions, self.scopes_d)
sys.stdout.write("Critic_Score = {0}".format(score))
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))
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 #after terminal state we gonna make all these variables to zero
self.episode_length = 0 #Now the AI need to start from new position
self.episode_max_q = -np.inf #after a terminaltion we do this
self.env.reset()
break
R = 0.0 #In the terminal Return is nothing #If it's terminal end we do not have a return from the final state
if not terminal_end: #But if it's not the turminal Return is the next value function
R = self.local_network.run_value(sess, self.env.s_t,
self.env.target, self.scopes)
#Agent's Samples
actions.reverse()
states.reverse()
rewards.reverse()
values.reverse()
#Expert's Samples
states_oracle.reverse()
actions_oracle.reverse()
actions_oracle.reverse()
#Agent's batch
batch_si = []
batch_a = []
batch_actions = []
batch_td = []
batch_R = []
batch_t = []
#Expert's Batch
batch_si_ex = []
batch_a_ex = []
batch_t_ex = []
batch_si_d = []
batch_t_d = []
batch_actions_d = []
#This is for the
for (s_e, a_e, t_e) in zip(states_oracle, actions_oracle,
targets_oracle):
batch_si_ex.append(s_e)
batch_a_ex.append(a_e)
batch_t_ex.append(t_e)
for (ai, si, ti) in zip(actions, states, targets):
batch_actions_d.append(ai)
batch_si_d.append(si)
batch_t_d.append(ti)
cur_learning_rate = self._anneal_learning_rate(global_t)
for i in range(10):
#sess.run(self.reset_gradients_d)
sess.run(
self.
accum_gradients_d, #since we update the algorithm for given action ,given state, given advatns and given value and given reward we do not care about the sequence
feed_dict={
self.local_discriminator.s_e: batch_si_ex,
self.local_discriminator.Actions_e: batch_a_ex,
self.local_discriminator.s_a: batch_si_d,
self.local_discriminator.Actions_a: batch_actions_d,
self.local_discriminator.t_e: batch_t_ex,
self.local_discriminator.t_a: batch_t_d
})
sess.run(
self.
apply_gradients_discriminator, #directly gradients get apply on the global discri
feed_dict={self.learning_rate_input: 0.00005})
loss = sess.run(self.local_discriminator.total_loss_d,
feed_dict={
self.local_discriminator.s_e: batch_si_ex,
self.local_discriminator.Actions_e: batch_a_ex,
self.local_discriminator.s_a: batch_si_d,
self.local_discriminator.Actions_a:
batch_actions_d,
self.local_discriminator.t_e: batch_t_ex,
self.local_discriminator.t_a: batch_t_d
})
sess.run(self.clip_local_d_weights
) #every update make sure u clip weihtfs
critic_r = self.local_discriminator.run_critic(sess, batch_si_d,
batch_t_d,
batch_actions_d,
self.scopes_d)
critic_r = critic_r * 0.1
rewards = rewards + critic_r #We concatenate the rewrds function
# Compute the advantage function , return and stack them as batches in Agent
for (ai, ri, si, Vi, ti) in zip(actions, rewards, states, values,
targets):
R = ri + GAMMA * R #calculatung the adcantage function
td = R - Vi
a = np.zeros([ACTION_SIZE])
a[ai] = 1 #making the actions one hot
batch_actions.append(ai)
batch_si.append(si)
batch_a.append(a)
batch_td.append(td)
batch_R.append(R)
batch_t.append(ti)
#syncying the new paramters to the old network in the thread PPO
sess.run(self.old_new_sync)
for i in range(4):
#sess.run(self.reset_gradients) #reset the gradients
sess.run(
self.
accum_gradients, #since we update the algorithm for given action ,given state, given advatns and given value and given reward we do not care about the sequence
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,
})
sess.run(
self.
apply_gradients_local, #apply the gradients to the local networ
feed_dict={self.learning_rate_input: cur_learning_rate})
#theoritcally we can have one accume gradient operation here
sess.run(self.apply_gradients,
feed_dict={self.learning_rate_input: cur_learning_rate})
sess.run(
self.sync_discriminator_l_G
) #syncing the paramters from the local network to the global newok
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
# check collision
if env.collided:
print('Collision occurs.')
env.collided = False
# check quit command
if stop_requested: break
viewer.imshow(env.observation)
if __name__ == '__main__':
env = Environment({
'scene_name': 'kitchen_02',
'terminal_state_id': 'chair1',
'checkpoint_state_id': 'k_c1'
})
# manually disable terminal states
#env.terminals = np.zeros_like(env.terminals)
#env.terminal_states, = np.where(env.terminals)
env.reset()
#print(env.s_position)
human_agent_action = None
human_wants_restart = False
stop_requested = False
viewer = SimpleImageViewer()
viewer.imshow(env.observation)
viewer.window.on_key_press = key_press
'/data/xinchao5')[1]
saver.restore(sess, checkpoint_path)
print("checkpoint loaded: {}".format(checkpoint_path))
else:
print("Could not find old checkpoint")
scene_stats = dict()
action_list = []
for scene_scope in scene_scopes:
scene_stats[scene_scope] = []
for task_scope in list_of_tasks[scene_scope]:
env = Environment({
'scene_name': 'bedroom_05',
'terminal_state_id': int(task_scope),
#'initial_state': EVAL_INIT_LOC,
})
real_target_xz.append(
[xz_numpy[int(task_scope)][0], xz_numpy[int(task_scope)][1]])
ep_rewards = []
ep_lengths = []
ep_collisions = []
scopes = [network_scope, scene_scope, task_scope]
#time.sleep(5)
if 1:
time.sleep(1)
cv2.imshow('target image', env.observation_target)
cv2.waitKey(0)
viewer = SimpleImageViewer()
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
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)
})
self.env.reset() #resetting the environment for each thread
states = [] #to keeep state ,actions ,targets and other stae
actions = []
rewards = []
values = []
targets = []
dones = []
terminal_end = False #in the start terminal state_end is false
# reset accumulated gradients
sess.run(
self.reset_gradients
) #resetting the gradient positions when starting the process for each
# copy weights from shared to local
sess.run(self.sync)
start_local_t = self.local_t
# t_max times loop
for i in range(
LOCAL_T_MAX
): #one thread will run for maximum amoound to 5 iterations then do a gradiet uodate
pi_, value_ = self.local_network.run_policy_and_value(
sess, self.env.s_t, self.env.target, self.scopes)
#pi_Old, value_Old = self.local_network.run_policy_and_value_old(sess, self.env.s_t, self.env.target, self.scopes)
action = self.choose_action(pi_)
states.append(self.env.s_t)
actions.append(action)
values.append(value_)
targets.append(self.env.target)
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 #getting the reward from the env
terminal = self.env.terminal #geting whether the agent went to a terminal state
# ad-hoc reward for navigation
reward = 10.0 if terminal else -0.01 #this is the normal reward here 10 if terminal all the others it is -0.01 (ollision donesst take in to the accout)
if self.episode_length > 5e3:
terminal = True #Here we do not let agent to run more that 5000 steps so we make it terminal
#but the above terminal thing has no effect on giving 10 as the rwaerd because we set the rweard above
self.episode_reward += reward
self.episode_length += 1
#this is what is the maximum value got in the episode
self.episode_max_q = max(self.episode_max_q, np.max(
value_)) #self.episode_max_q-This is -inf in the beggining
# clip reward
rewards.append(
np.clip(reward, -1, 1)
) #make sure the rewartds is between -1 and +1 even thore rtthere is a 10
self.local_t += 1
# s_t1 -> s_t
self.env.update()
if terminal: #if we go to the terminal state we will surely break the function
sys.stdout.write("Pi = {0} V = {1}\n".format(pi_, value_))
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))
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 #after terminal state we gonna make all these variables to zero
self.episode_length = 0 #Now the AI need to start from new position
self.episode_max_q = -np.inf #after a terminaltion we do this
self.env.reset()
break
'''
Here I should call the discriminator and get the reward signal from that
R_D=sess.run(D.get_reward(state,action))
'''
R = 0.0 #In the terminal Return is nothing #If it's terminal end we do not have a return from the final state
if not terminal_end: #But if it's not the turminal Return is the next value function
R = self.local_network.run_value(sess, self.env.s_t,
self.env.target, self.scopes)
Returns = np.zeros_like(rewards)
Advants = np.zeros_like(rewards)
lastgaelam = 0
LAMBDA = 0.9
GAM = 0.9
self.nsteps = len(rewards)
############################################################################# we should assined all params to the new params
#This will only has an effect on
#####################################################################
for t in reversed(range(self.nsteps)):
if t == self.nsteps - 1:
nextnonterminal = 1.0 - bool(
R == 0) #if R ==0 means the agent found the terminal stage
nextvalues = R
else:
nextnonterminal = 1.0 - bool(R == 0)
nextvalues = values[t + 1]
delta = rewards[t] + GAM * nextvalues * nextnonterminal - values[t]
Advants[
t] = lastgaelam = delta + GAM * LAMBDA * lastgaelam * nextnonterminal
Returns[t] = Advants[t] + values[t]
#Returns=Advants+values #This is more of the v_next
Advants = (Advants - Advants.mean()) / (Advants.std() + 1e-5)
#Returns=(Returns - Returns.mean()) / (Returns.std() + 1e-5)
Returns = Returns.tolist()
Advants = Advants.tolist()
actions.reverse()
states.reverse()
rewards.reverse()
values.reverse()
Returns.reverse()
Advants.reverse()
batch_si = []
batch_a = []
batch_td = []
batch_R = []
batch_t = []
batch_advant = []
batch_Return = []
# compute and accmulate gradients
for (ai, ri, si, Vi, ti, Re, Ad) in zip(actions, rewards, states,
values, targets, Returns,
Advants):
R = ri + GAMMA * R #calculatung the adcantage function
td = R - Vi
a = np.zeros([ACTION_SIZE])
a[ai] = 1 #making the actions one hot
batch_si.append(si)
batch_a.append(a)
batch_td.append(td)
batch_R.append(R)
batch_t.append(ti)
batch_advant.append(Ad)
batch_Return.append(Re)
sess.run(self.old_new_sync)
cur_learning_rate = self._anneal_learning_rate(global_t)
for i in range(3):
sess.run(
self.
accum_gradients, #since we update the algorithm for given action ,given state, given advatns and given value and given reward we do not care about the sequence
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.Returns: batch_Return,
self.local_network.Advantages: batch_advant
})
sess.run(self.apply_gradients_local,
feed_dict={self.learning_rate_input: cur_learning_rate})
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
def process(self, sess, global_t, summary_writer, summary_op,
summary_placeholders): #This is to run the process
if self.env is None:
# lazy evaluation
time.sleep(self.thread_index * 1.0)
self.env = Environment(
{ #This is where you access in to the environment #scene_loader import THORDiscreteEnvironment
'scene_name': self.scene_scope,
'terminal_state_id': int(self.task_scope)
})
self.env.reset()
self.oracle = ShortestPathOracle(
self.env, ACTION_SIZE
) #Get the probabilities of the shortest paths to go to exat position
states = []
targets = []
oracle_pis = [] #expert policies
terminal_end = False
if self.mode is "train": #if the trainign is there
# reset accumulated gradients
sess.run(self.reset_gradients) #reet all the gradients
# copy weights from shared to local
sess.run(self.sync) #
start_local_t = self.local_t
# t_max times loop (5 steps)
for i in range(LOCAL_T_MAX): # This is for the training
flipped_run = self.encourage_symmetry and np.random.random() > 0.5
if flipped_run:
s_t = self.env.target
g = self.env.s_t
else:
s_t = self.env.s_t
g = self.env.target #first the initial state start with same state 4 times as the history stacked as frames 2048*5
smashnet_pi = self.local_network.run_policy(
sess, s_t, g,
self.scopes) #now gethe policy frmo the local network
if flipped_run: smashnet_pi = self._flip_policy(smashnet_pi)
oracle_pi = self.oracle.run_policy(
self.env.current_state_id
) #get the policy of the oracle which means the shotest path kind of action in the given state
diffidence_rate = self._anneal_diffidence_rate(global_t)
action = self.choose_action(smashnet_pi, oracle_pi,
diffidence_rate)
states.append(s_t) #stack action
targets.append(g) #stack target position
if flipped_run: oracle_pis.append(self._flip_policy(oracle_pi))
else: oracle_pis.append(oracle_pi) #get the expert's policies
# if VERBOSE and global_t % 10000 == 0:
# print("Thread %d" % (self.thread_index))
# sys.stdout.write("SmashNet Pi = {}, Oracle Pi = {}\n".format(["{:0.2f}".format(i) for i in smashnet_pi], ["{:0.2f}".format(i) for i in oracle_pi]))
if VALIDATE and global_t % VALIDATE_FREQUENCY == 0 and global_t > 0 and self.thread_index == 0: #This is for the alidation of the results
results = self._evaluate(sess,
list_of_tasks=VALID_TASK_LIST,
num_episodes=NUM_VAL_EPISODES,
max_steps=MAX_VALID_STEPS,
success_cutoff=SUCCESS_CUTOFF)
print("Thread %d" % (self.thread_index))
print("Validation results: %s" % (results))
self.env.step(action) #here we change the next step
is_terminal = self.env.terminal or self.episode_length > 5e3
if self.mode is "val" and self.episode_length > 1e3:
is_terminal = True
self.episode_length += 1
self.episode_pi_sim += 1. - cosine(smashnet_pi, oracle_pi)
self.local_t += 1
# s_t1 -> s_t
self.env.update() #update the new state
if is_terminal:
terminal_end = True
if self.mode is "val":
sess.run(self.sync)
sys.stdout.write(
"time %d | thread #%d | scene %s | target %s | episode length = %d\n"
% (global_t, self.thread_index, self.scene_scope,
self.task_scope, self.episode_length))
summary_values = {
"episode_length_input":
float(self.episode_length),
"episode_pi_sim_input":
self.episode_pi_sim / float(self.episode_length),
"episode_loss_input":
float(self.episode_loss)
}
self._record_score(sess, summary_writer, summary_op,
summary_placeholders, summary_values,
global_t)
self.episode_length = 0
self.episode_pi_sim = 0
self.episode_loss = 0
self.env.reset()
break
if self.mode is "train":
states.reverse()
oracle_pis.reverse()
batch_si = []
batch_ti = []
batch_opi = []
# compute and accmulate gradients
for (si, ti, opi) in zip(states, targets, oracle_pis):
batch_si.append(si)
batch_ti.append(ti)
batch_opi.append(opi)
sess.run(self.accum_gradients,
feed_dict={
self.local_network.s: batch_si,
self.local_network.t: batch_ti,
self.local_network.opi: batch_opi
})
self.episode_loss += sum(
sess.run(self.local_network.loss,
feed_dict={
self.local_network.s: batch_si,
self.local_network.t: batch_ti,
self.local_network.opi: batch_opi
}))
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
def main(args):
scene_scope = 'bathroom_02'
task_scope = 26 #26 43 53 32 41
env = Environment({
'scene_name': scene_scope,
'terminal_state_id': int(task_scope)
})
S_Class = SIAMESE() #Creating a siamese class -object
Policy = Policy_net(
'policy', S_Class) #buiding the actor critic graph / object , Passing
Old_Policy = Policy_net('old_policy',
S_Class) #same thing as the other PPO
PPO = PPOTrain(Policy, Old_Policy,
gamma=args.gamma) #gradiet updatror object or the graph
D = Discriminator(S_Class) #discriminator of the Gan Kind of thing
'''
batch_n=tf.get_collection(tf.GraphKeys.UPDATE_OPS)
var_list=tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES, scope='Siamese')
'''
#Loading Expert Data State/Tragets etc
expert_observations = np.genfromtxt(
'trajectory/observations.csv') #load expert demnetrations
expert_targets = np.genfromtxt('trajectory/targets.csv')
expert_actions = np.genfromtxt('trajectory/actions.csv', dtype=np.int32)
expert_observations = np.reshape(expert_observations,
newshape=[-1, 2048, 4])
expert_targets = np.reshape(expert_targets, newshape=[-1, 2048, 4])
saver = tf.train.Saver(
) #Assign another save if you want to use BC weights
if args.restore: #We need a seperate saver only for assigning paramters from BC trained thing
saver2 = tf.tran.Saver([
tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES, scope='policy'),
tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES, scope='Siamese')
])
with tf.Session() as sess:
writer = tf.summary.FileWriter(args.logdir, sess.graph)
sess.run(
tf.global_variables_initializer()
) #here already variables get intialized both old policy and new policy net
if args.restore:
if args.model == '':
saver2.restore(
sess,
args.modeldir + '/' + args.alg + '/' + 'shamane.ckpt')
print("Model Reastored")
else:
saver.restore(
sess, args.modeldir + '/' + args.alg + '/' +
'model.ckpt-' + args.model)
success_num = 0 #This is use to check whether my agent went to the terminal point
#var_list=tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES)
for iteration in range(
100000): #args.iteration):#Here start the adversial training
print(
"Starting ........ The Iteration---------------------------------------------------- :",
iteration)
observations = []
actions = []
#rewards = []
targets = [] #for the gail
v_preds = []
run_policy_steps = 0
while (
True
): #Here what is happenning is , this again samples trajectories from untrain agent
run_policy_steps += 1
obs = np.stack([env.s_t]).astype(
dtype=np.float32
) # prepare to feed placeholder Policy.obs #Initial observation
target = np.stack([env.s_target]).astype(
dtype=np.float32
) #This is to make sure that input is [batch_size,2048,4]
act, v_pred, prob = Policy.act(
state=obs, target=target,
stochastic=True) # Agents action and values
act = np.asscalar(act)
v_pred = np.asscalar(v_pred)
observations.append(obs) #save the set of observations
targets.append(target)
actions.append(act) #save the set of actions
v_preds.append(v_pred)
#next_obs, reward, done, info = env.step(act) #get the next observation and reward acording to the observation
next_obs, is_terminal, is_collided = env.step(act)
if is_terminal:
success_num = success_num + 1
print(
"Congratz yoy just reach the terminal state which is:",
env.terminal_state_id)
if is_collided:
print(
"Bad Luck your agent just collided couldn't made it to the terminal state which is :",
env.terminal_state_id)
if (is_terminal or is_collided
or (run_policy_steps
== 100)): #run one episode till the termination
print("Number Of Exploration by the AGENT:",
run_policy_steps)
v_preds_next = v_preds[1:] + [
0
] # next state of terminate state has 0 state value #this list use to update the parameters of the calue net
print(
"Environment is resetting after the collition/Terminal"
)
obs = env.reset()
#reward = -1
break #with tihs vreak all obsercation ,action and other lists get empty
#print(sum(rewards))
writer.add_summary(
tf.Summary(value=[
tf.Summary.Value(tag='episode_length',
simple_value=run_policy_steps)
]), iteration)
#writer.add_summary(tf.Summary(value=[tf.Summary.Value(tag='episode_reward', simple_value=sum(rewards))])
#, iteration)
if success_num >= 5000:
saver.save(sess, args.savedir + '/model.ckpt')
print('Clear!! Model saved.')
break
#else:
#success_num = 0
# convert list to numpy array for feeding tf.placeholder
observations = np.reshape(observations,
newshape=[-1, 2048,
4]) #collect observations
targets = np.reshape(targets, newshape=[-1, 2048, 4])
actions = np.array(actions).astype(
dtype=np.int32) #collect the actions
# train discriminator #Here comes the Discriminator !!
Dis_input = [
expert_observations, expert_targets, expert_actions,
observations, targets, actions
]
observations.shape[0]
expert_observations.shape[0]
if observations.shape[0] < expert_observations.shape[0]:
High = observations.shape[0]
else:
High = expert_observations.shape[0]
for i in range(100):
sample_indices = np.random.randint(low=0, high=High, size=32)
sampled_inp_D = [
np.take(a=a, indices=sample_indices, axis=0)
for a in Dis_input
]
D.train(expert_s=sampled_inp_D[0],
expert_t=sampled_inp_D[1],
expert_a=sampled_inp_D[2],
agent_s=sampled_inp_D[3],
agent_t=sampled_inp_D[4],
agent_a=sampled_inp_D[5])
'''
D.train(expert_s=expert_observations,
expert_t=expert_targets,
expert_a=expert_actions,
agent_s=observations,
agent_t=targets,
agent_a=actions)
'''
#To get rewards we can use a RNN , then we can get the each time unit output to collect the reward function
d_rewards = D.get_rewards(
agent_s=observations, agent_t=targets, agent_a=actions
) #how well our agent performed with respect to the expert
d_rewards = np.reshape(d_rewards, newshape=[-1]).astype(
dtype=np.float32) #rewards for each action pair
gaes = PPO.get_gaes(
rewards=d_rewards, v_preds=v_preds, v_preds_next=v_preds_next
) #this to calcuate the advantage function in PPO
gaes = np.array(gaes).astype(dtype=np.float32)
# gaes = (gaes - gaes.mean()) / gaes.std()
v_preds_next = np.array(v_preds_next).astype(
dtype=np.float32) #This is the next value function
#train policy
inp = [
observations, targets, actions, gaes, d_rewards, v_preds_next
]
PPO.assign_policy_parameters(
) #Assigning policy params means assigning the weights to the default policy nets
for epoch in range(
100
): #This is to train the Agent (Actor Critic ) from the obtaiend agent performances and already trained discriminator
sample_indices = np.random.randint(
low=0, high=observations.shape[0],
size=32) # indices are in [low, high)
sampled_inp = [
np.take(a=a, indices=sample_indices, axis=0) for a in inp
] # Here trainign the policy network
PPO.train(state=sampled_inp[0],
targets=sampled_inp[1],
actions=sampled_inp[2],
gaes=sampled_inp[3],
rewards=sampled_inp[4],
v_preds_next=sampled_inp[5])
summary = PPO.get_summary(obs=inp[0],
target=inp[1],
actions=inp[2],
gaes=inp[3],
rewards=inp[4],
v_preds_next=inp[5])
writer.add_summary(summary, iteration)
writer.close()
def train(rank,
scene_scope,
task_scope,
args,
shared_model,
counter,
lock,
optimizer=None):
torch.manual_seed(args.seed + rank)
#env = create_atari_env(args.env_name)
#env.seed(args.seed + rank)
env = Environment({
'scene_name': scene_scope,
'terminal_state_id': int(task_scope)
})
model = ActorCriticFFNetwork(ACTION_SIZE)
if optimizer is None:
# TODO: Discount learning rate based on episode length
optimizer = my_optim.SharedRMSprop(shared_model.parameters(),
lr=args.lr,
alpha=args.alpha,
eps=args.eps)
optimizer.share_memory()
model.train()
env.reset()
state = torch.from_numpy(env.s_t)
done = True
episode_length = 0
for i in range(int(args.max_episode_length)):
# Sync with the shared model
model.load_state_dict(shared_model.state_dict())
'''
if done:
cx = Variable(torch.zeros(1, 256))
hx = Variable(torch.zeros(1, 256))
else:
cx = Variable(cx.data)
hx = Variable(hx.data)
'''
values = []
log_probs = []
rewards = []
entropies = []
for step in range(args.num_steps):
print('Thread: ', rank, ', step: ', step, 'epochs:', i)
episode_length += 1
logit, value = model(env.s_t, env.target)
prob = F.softmax(logit, dim=1)
log_prob = F.log_softmax(logit, dim=1)
entropy = -(log_prob * prob).sum(1, keepdim=True)
entropies.append(entropy)
action = prob.multinomial(num_samples=1).data
log_prob = log_prob.gather(1, Variable(action))
env.step(action)
#state, reward, done, _ = env.step(action.numpy())
env.update()
state = env.s_t
reward = env.reward
done = env.terminal
done = done or episode_length >= args.max_episode_length
reward = max(min(reward, 1), -1)
with lock:
if counter.value % 1000 == 0:
print('Now saving data. Please wait.')
torch.save(shared_model.state_dict(),
CHECKPOINT_DIR + '/' + 'checkpoint.pth.tar')
counter.value += 1
if done:
episode_length = 0
if env.terminal:
print('Task completed')
counter.value += 1
if done:
episode_length = 0
env.reset()
state = env.s_t
state = torch.from_numpy(state)
values.append(value)
log_probs.append(log_prob)
rewards.append(reward)
if done:
break
R = torch.zeros(1, 1)
if not done:
_, value = model(env.s_t, env.target)
R = value.data
values.append(Variable(R))
policy_loss = 0
value_loss = 0
R = Variable(R)
gae = torch.zeros(1, 1)
for i in reversed(range(len(rewards))):
R = args.gamma * R + rewards[i]
advantage = R - values[i]
value_loss = value_loss + 0.5 * advantage.pow(2)
# Generalized Advantage Estimataion
delta_t = rewards[i] + args.gamma * \
values[i + 1].data - values[i].data
gae = gae * args.gamma * args.tau + delta_t
policy_loss = policy_loss - \
log_probs[i] * Variable(gae) - args.entropy_coef * entropies[i]
optimizer.zero_grad()
(policy_loss + args.value_loss_coef * value_loss).backward()
torch.nn.utils.clip_grad_norm_(model.parameters(), args.max_grad_norm)
ensure_shared_grads(model, shared_model)
optimizer.step()
saver.restore(sess, checkpoint.model_checkpoint_path)
print("checkpoint loaded: {}".format(checkpoint.model_checkpoint_path))
else:
print("Could not find old checkpoint")
scene_stats = dict()
for scene_scope in scene_scopes:
scene_stats[scene_scope] = []
for task_scope in list_of_tasks[scene_scope]:
print(task_scope)
print("______________________________________________")
env = Environment({
'scene_name': scene_scope,
'terminal_state_id': int(task_scope)
})
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
def save_expert(self):
states = []
action_list = []
target_list = []
action_history_list = []
for i in range(10):
self.env = Environment({
'scene_name': self.scene_scope,
'terminal_state_id': int(self.task_scope)
})
print(
"Starting -",
"*******************************************************************-----------",
i)
print("From the environment Current State ID--",
self.env.current_state_id)
print("From the environment Traget State ID--",
self.env.terminal_state_id)
print("Frm the environment Number of possible states",
self.env.n_locations)
print(
"________________________________________________________________________________"
)
self.oracle = ShortestPathOracle(self.env, ACTION_SIZE)
action_itr = 0 #To stack action history
while (not (self.env.terminal)):
s_t = self.env.s_t
target = self.env.s_target
#self.oracle = ShortestPathOracle(self.env, ACTION_SIZE)
oracle_pi = self.oracle.run_policy(
self.env.current_state_id
) #get the policy of the oracle which means the shotest path kind of action in the given state
action = self.choose_action(
oracle_pi) #select the action probabilistically
if action_itr == 0:
action_his = np.tile(action, (4))
action_itr = 1
else:
action_his = np.append(action_his[1:], action)
#Saving current state,targets and action thriplets
states.append(s_t) #stack action
action_list.append(action)
target_list.append(target)
action_history_list.append(action_his)
self.env.step(action) #here we change the next step
is_terminal = self.env.terminal
is_collided = self.env.collided
self.local_t += 1
if is_collided:
print(
"Wrong action-------- Error Error Error Error Error Error Error Error Error Error"
)
break
# s_t1 -> s_t
self.env.update() #update the new state
#self.env.reset() #With this
print("Done with one epoach one start state to end goal ")
self.env.reset()
states = np.reshape(states, newshape=[-1] + list([8192]))
target_list = np.reshape(target_list, newshape=[-1] + list([8192]))
self.open_file_and_save('trajectory/observations.csv', states)
print(np.shape(states))
print(np.shape(action_list))
print(np.shape(action_history_list))
print(np.shape(target_list))
self.open_file_and_save('trajectory/actions.csv', action_list)
self.open_file_and_save('trajectory/targets.csv', target_list)
self.open_file_and_save('trajectory/actions_history.csv',
action_history_list)
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.task_scope_name = random.randint(1, 468) - 1
self.env = Environment({
'scene_name': self.scene_scope,
'terminal_state_id': self.task_scope_name
})
self.env.reset()
states = []
actions = []
rewards = []
values = []
targets = []
terminal_end = False
# reset accumulated gradients
sess.run(self.reset_gradients)
# copy weights from shared to local
sess.run(self.sync)
start_local_t = self.local_t
# t_max times loop
for i in range(LOCAL_T_MAX):
pi_, value_ = self.local_network.run_policy_and_value(
sess, self.env.s_t, self.env.target, self.scopes)
action = self.choose_action(pi_)
states.append(self.env.s_t)
actions.append(action)
values.append(value_)
targets.append(self.env.target)
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 terminal:
terminal_end = True
print '----------'
print('real terminal id is {}'.format(self.task_scope_name))
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))
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.task_scope_name = random.randint(1, 468) - 1
self.env = Environment({
'scene_name':
self.scene_scope,
'terminal_state_id':
self.task_scope_name
})
self.env.reset()
print('init id is {}'.format(self.env.current_state_id))
print '----------'
break
R = 0.0
if not terminal_end:
R = self.local_network.run_value(sess, self.env.s_t,
self.env.target, self.scopes)
actions.reverse()
states.reverse()
rewards.reverse()
values.reverse()
batch_si = []
batch_a = []
batch_td = []
batch_R = []
batch_t = []
# compute and accmulate gradients
for (ai, ri, si, Vi, ti) in zip(actions, rewards, states, values,
targets):
R = ri + GAMMA * R
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_t.append(ti)
if USE_LSTM:
batch_si.reverse()
batch_a.reverse()
batch_td.reverse()
batch_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.initial_lstm_state:
start_lstm_state,
})
# self.local_network.step_size: [len(batch_a)]
else:
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
})
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
else:
checkpoint_path = checkpoint.model_checkpoint_path
saver.restore(sess, checkpoint_path)
print("checkpoint loaded: {}".format(checkpoint_path))
else:
print("Could not find old checkpoint")
scene_stats = dict()
for scene_scope in scene_scopes:
scene_stats[scene_scope] = []
for task_scope in list_of_tasks[scene_scope]:
env = Environment({
'scene_name': scene_scope,
'terminal_state_id': int(task_scope),
'initial_state': EVAL_INIT_LOC,
})
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
def test(rank, scene_scope, task_scope, args, shared_model, counter):
torch.manual_seed(args.seed + rank)
env = Environment({
'scene_name': scene_scope,
'terminal_state_id': int(task_scope)
})
model = ActorCriticFFNetwork(ACTION_SIZE)
model.eval()
height, width, layers = env.observation.shape
video = cv2.VideoWriter('video/' + task_scope + '.mp4',-1,1,(width,height))
env.reset()
state = torch.from_numpy(env.s_t)
reward_sum = 0
done = True
start_time = time.time()
# a quick hack to prevent the agent from stucking
actions = deque(maxlen=100)
episode_length = 0
img = cv2.cvtColor(env.observation, cv2.COLOR_BGR2RGB)
video.write(img)
for i in range(100):
episode_length += 1
# Sync with the shared model
if done:
model.load_state_dict(shared_model.state_dict())
logit, value = model(env.s_t, env.target)
prob = F.softmax(logit, dim=1)
action = prob.max(1, keepdim=True)[1].data.numpy()
env.step(action[0, 0])
env.update()
img = cv2.cvtColor(env.observation, cv2.COLOR_BGR2RGB)
video.write(img)
reward = env.reward
state = env.s_t
done = env.terminal
print(env.terminal_state_id, env.current_state_id)
done = done or episode_length >= args.max_episode_length
reward_sum += reward
# a quick hack to prevent the agent from stucking
actions.append(action[0, 0])
if actions.count(actions[0]) == actions.maxlen:
done = True
if done:
print("Time {}, num steps {}, FPS {:.0f}, episode reward {}, episode length {}".format(
time.strftime("%Hh %Mm %Ss",
time.gmtime(time.time() - start_time)),
counter.value, counter.value / (time.time() - start_time),
reward_sum, episode_length))
reward_sum = 0
episode_length = 0
actions.clear()
env.reset()
state = env.s_t
break
state = torch.from_numpy(state)
cv2.destroyAllWindows()
video.release()
saver.restore(sess, checkpoint_path)
print("checkpoint loaded: {}".format(checkpoint_path))
else:
print("Could not find old checkpoint")
scene_stats = dict()
action_list = []
flag_i = 0
for scene_scope in scene_scopes:
scene_stats[scene_scope] = []
for task_scope in list_of_tasks[scene_scope]:
env = Environment({
'scene_name': SCENE_NAME,
'terminal_state_id': int(task_scope),
#'initial_state': EVAL_INIT_LOC,
})
real_target_xz.append(
[xz_numpy[int(task_scope)][0], xz_numpy[int(task_scope)][1]])
ep_rewards = []
ep_lengths = []
ep_collisions = []
scopes = [network_scope, scene_scope, task_scope]
#time.sleep(5)
a = np.arange(300 * 400 * 3)
imx = a.reshape(300, 400, 3).astype('uint8')
# im0 = img_from_thor()
#env.last_event.frame.astype('float32')
show_im = env.observation_target
