def main():
map_dir = "/home/i2rlab/shahil_files/shahil_RL_ws_new/src/turtlebot/turtlebot_gazebo/worlds/"
#os.rename(map_dir+"house_train.world",map_dir+"maze.world")
rospy.init_node('turtlebot2_human', anonymous=True, log_level=rospy.WARN)
env = StartOpenAI_ROS_Environment(
'MyTurtleBot2HumanModel-v1')
#os.rename(map_dir+"maze.world",map_dir+"house_train.world")
s_dim = env.observation_space.shape[0]+4
a_dim = env.action_space.shape[0]
a_bound = env.action_space.high
#print('a_bound test', a_bound)
sign_talker = rospy.Publisher('/sign', Int64, queue_size=1)
####################### load agent #######################################
#TD3 = td3(a_dim, s_dim, a_bound, GAMMA, TAU, MEMORY_CAPACITY, BATCH_SIZE, LR_A, LR_C)
#TD3.loader()
d = [0.5,1,0.5,1]+list(6*np.ones([180]))
#A = np.array([[1,0,0,0],[0,1,0,0],[0,0,1,0],[0,0,0,1]])
#TGREEN = '\033[32m' # Green Text
#ENDC = '\033[m' # reset to the defaults
#################### Start ####################
i=0
while i <2:
s = env.reset()
#print("env printing: ",env)
t1 = time.time()
min_distance_ob = s[-2]
theta_bt_uh_ob = s[-1]
#a_old = A[0]
a_old = np.array([1,0,0,0])
s = np.hstack((a_old,np.array(s[3:-2])/d))
for j in range(MAX_EP_STEPS):
################ Mode #########################################
#if i%2==0:
#a = TD3.choose_action(s,1)
#a = [1,0,0,0]
#if a[1]<=0.9 and min_distance_ob<=0.7 and abs(theta_bt_uh_ob)<1.3:
#print (TGREEN + "Going to Crash!" , ENDC)
#a = A[1]
'''else:
a = A[0]'''
#x_test = env.state_msg.pose.position.x
#print('x_test',env.collect_data)
a = np.array([1,0,0,0])
s_, r, done, info = env.step(a)
sign_talker.publish(int(r))
if r <-200:
done = 0
min_distance_ob = s_[-2]
theta_bt_uh_ob = s_[-1]
a_old = a
s=np.hstack((a_old,np.array(s_[3:-2])/d))
if done or j == MAX_EP_STEPS-1:
t = time.time()-t1
if j>3:
i=i+1
break
def test(self):
# Initialize OpenAI_ROS ENV
LoadYamlFileParamsTest(rospackage_name="learning_ros", rel_path_from_package_to_file="config", yaml_file_name="aliengo_stand.yaml")
env = StartOpenAI_ROS_Environment('AliengoStand-v0')
saver = tf.train.Saver()
time.sleep(3)
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
saver.restore(sess, 'model_test/model_ddqn.ckpt')
scores = []
success_num = 0
for e in range(self.EPISODES):
state = env.reset()
state = np.reshape(state, [1, self.state_size])
done = False
i = 0
rewards = []
while not rospy.is_shutdown(): # until ros is not shutdown #self.env.render()
# height prob is chosen as action
action = np.argmax(self.model.predict(state))
next_state, reward, done, _ = env.step(action)
time.sleep(0.01)
next_state = np.reshape(next_state, [1, self.state_size])
rewards.append(reward)
self.remember(state, action, reward, next_state, done)
i += 1
if done:
# every step update target model
self.update_target_model()
state = env.reset()
break
else:
state = next_state
self.replay()
scores.append(sum(rewards))
plt.plot(scores)
plt.show()
initial_epsilon = qlearn.epsilon
start_time = time.time()
highest_reward = 0
# Starts the main training loop: the one about the episodes to do
for x in range(nepisodes):
rospy.logdebug("############### WALL START EPISODE=>" + str(x))
cumulated_reward = 0
done = False
if qlearn.epsilon > 0.05:
qlearn.epsilon *= epsilon_discount
# Initialize the environment and get first state of the robot
observation = env.reset()
state = ''.join(map(str, observation))
# Show on screen the actual situation of the robot
# env.render()
# for each episode, we test the robot for nsteps
for i in range(nsteps):
rospy.logwarn("############### Start Step=>" + str(i))
# Pick an action based on the current state
action = qlearn.chooseAction(state)
rospy.logwarn("Next action is:%d", action)
# Execute the action in the environment and get feedback
observation, reward, done, info = env.step(action)
rospy.logwarn(str(observation) + " " + str(reward))
cumulated_reward += reward
episode_lens = np.zeros(num_episodes)
epsilon = 1.0
epsilon_min = 0.1
epsilon_change = (epsilon - epsilon_min) / 500000
rospy.init_node('sumo_dqlearn', anonymous=True, log_level=rospy.WARN)
episode_counter_pub = rospy.Publisher('/episode_counter', Int16)
# Init OpenAI_ROS ENV
task_and_robot_environment_name = rospy.get_param(
'/turtlebot2/task_and_robot_environment_name')
env = StartOpenAI_ROS_Environment(task_and_robot_environment_name)
rospy.loginfo("Gym environment done")
obs = env.reset()
print obs.shape
# Create the Gym environment
rospy.loginfo("Gym environment done")
rospy.loginfo("Starting Learning")
# Set the logging system
rospack = rospkg.RosPack()
pkg_path = rospack.get_path('gazebo_sumo')
#outdir = pkg_path + '/training_results'
#env = wrappers.Monitor(env, outdir, force=True)
rospy.loginfo("Monitor Wrapper started")
last_time_steps = np.ndarray(0)
rospy.init_node('turtlebot2_wall_random', anonymous=True, log_level=rospy.WARN)
task_and_robot_environment_name = rospy.get_param(
'/turtlebot2/task_and_robot_environment_name')
env = StartOpenAI_ROS_Environment(task_and_robot_environment_name)
print("Environment: ", env)
print("Action space: ", env.action_space)
# print(env.action_space.high)
# print(env.action_space.low)
print("Observation space: ", env.observation_space)
print(env.observation_space.high)
print(env.observation_space.low)
for episode in range(20):
env.reset()
for t in range(100):
action = env.action_space.sample()
obs, reward, done, info = env.step(action)
print("episode: ", episode)
print("timestep: ", t)
print("obs: ", obs)
print("action:", action)
print("reward: ", reward)
print("done: ", done)
print("info: ", info)
if done:
basedirpathlogs = os.path.join(basedir, "logs")
logdir = os.path.join(basedirpathlogs, datetime.datetime.now().strftime("%Y%m%d-%H%M%S"))
writer = SummaryWriter(log_dir=logdir)
#tracedir = "$HOME/python3_ws/src/turtle2_openai_ros_example/src/trace"
tracedir = os.path.join(basedir, "trace")
############################################################################
print("Target reward: {}".format(env.spec.reward_threshold))
step_count = 0
ep_rew_history = []
i_episode, ep_reward = 0, -float('inf')
while step_count < num_steps:
rospy.logdebug("############### START EPISODE=>" + str(i_episode))
# Initialize the environment and state
# type(state): <class 'list'>
state, done = env.reset(), False
state = [round(num, 1) for num in state]
list_state = state
#print("\n type(state): ")
#print(type(state))
rospy.logwarn("# state we are => " + str(state))
state = torch.from_numpy(np.array(state)).float().unsqueeze(0).to(device)
while not done:
rospy.logwarn("i_episode: " + str(i_episode))
rospy.logwarn("step_count: " + str(step_count))
# Select an action
eps_greedy_threshold = compute_eps_threshold(step_count, eps_start, eps_end, eps_decay)
action, policy_act, policy_used = select_action(policy_net, state, device, env, eps_greedy_threshold, n_actions)
rospy.logwarn("Next action is:%d", action)
# Perform action in env
def run(self):
# Initialize OpenAI_ROS ENV
LoadYamlFileParamsTest(rospackage_name="learning_ros",
rel_path_from_package_to_file="config",
yaml_file_name="aliengo_stand.yaml")
env = StartOpenAI_ROS_Environment('AliengoStand-v0')
saver = tf.train.Saver()
time.sleep(3)
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
scores = []
success_num = 0
for e in range(self.EPISODES):
state = env.reset()
state = np.reshape(state, [1, self.state_size])
done = False
rewards = []
while not rospy.is_shutdown(
): # until ros is not shutdown #self.env.render()
action = self.act(state)
next_state, reward, done, _ = env.step(action)
time.sleep(0.01)
next_state = np.reshape(next_state, [1, self.state_size])
rewards.append(reward)
self.remember(state, action, reward, next_state, done)
if done:
# every step update target model
self.update_target_model()
state = env.reset()
break
else:
state = next_state
self.replay()
#if consectuvely 10 times has high reward end the training
scores.append(sum(rewards))
if sum(rewards) >= 400:
success_num += 1
print("Succes number: " + str(success_num))
if success_num >= 5: #checkpoint
if (ddqn):
saver.save(sess, 'model_train/model_ddqn.ckpt')
else:
saver.save(sess, 'model_train/model_dqn.ckpt')
print('Clear!! Model saved.')
if success_num >= 10:
if (ddqn):
saver.save(sess, 'model_train/model_ddqn.ckpt')
else:
saver.save(sess, 'model_train/model_dqn.ckpt')
print('Clear!! Model saved. AND Finished! ')
break
else:
success_num = 0
plt.plot(scores)
plt.show()
def main():
name = input('Please input your exp number:')
folder = "Human_exp_new_" + str(name)
os.system("mkdir " + folder)
####################### inite environment ########################################
map_dir = "/home/i2rlab/shahil_files/shahil_RL_ws_new/src/turtlebot/turtlebot_gazebo/worlds/"
#os.rename(map_dir+"house_3.world",map_dir+"maze.world")
rospy.init_node('turtlebot2_human', anonymous=True, log_level=rospy.WARN)
env = StartOpenAI_ROS_Environment('MyTurtleBot2HumanModel-v1')
#os.rename(map_dir+"maze.world",map_dir+"house_3.world")
s_dim = env.observation_space.shape[0] + 4
a_dim = env.action_space.shape[0]
a_bound = env.action_space.high
sign_talker = rospy.Publisher('/sign', Int64, queue_size=1)
####################### load agent #######################################
TD3 = td3(a_dim, s_dim, a_bound, GAMMA, TAU, MEMORY_CAPACITY, BATCH_SIZE,
LR_A, LR_C)
TD3.loader()
# ddpg = DDPG(a_dim, s_dim, a_bound, GAMMA, TAU, MEMORY_CAPACITY, BATCH_SIZE, LR_A, LR_C)
# with ddpg.sess.as_default():
# with ddpg.g.as_default():
# ddpg.loader()
d = [0.5, 1, 0.5, 1] + list(6 * np.ones([180]))
A = np.array([[1, 0, 0, 0], [0, 1, 0, 0], [0, 0, 1, 0], [0, 0, 0, 1]])
TGREEN = '\033[32m' # Green Text
ENDC = '\033[m' # reset to the defaults
#################### Initialize Data #################################
n_h = 0 ############## number of eps of pure human control #################
n_s_TD3 = 0 ############## number of eps of shared control #################
# n_s_ddpg = 0
n_crash_h = 0 ############## number of crash in pure human control #################
n_crash_s_TD3 = 0 ############## number of crash in shared control #################
# n_crash_s_ddpg = 0
n_success_h = 0 ############## number of success of pure human control #################
n_success_s_TD3 = 0 ############## number of success of shared control #################
# n_success_s_ddpg = 0
t_h = 0 ############## total time of pure human control #################
t_s_TD3 = 0 ############## total time of shared control #################
# t_s_ddpg = 0
#################### SART #####################
SA_h = 0
SA_s_TD3 = 0
# SA_s_ddpg = 0
#################### workload #################
WL_h = 0
WL_s_TD3 = 0
# WL_s_ddpg = 0
#################### Start ####################
i = 0
total_ep = 2
sequence_ep = [0, 1]
#np.random.shuffle(sequence_ep)
pureh_ep = sequence_ep[0:int(total_ep / 2)]
# ddpg_ep = sequence_ep[int(total_ep/3):int(2*total_ep/3)]
td3_ep = sequence_ep[int(total_ep / 2):]
n_d = 0
while i < total_ep:
s = env.reset()
print(s)
t1 = time.time()
min_distance_ob = s[-2]
theta_bt_uh_ob = s[-1]
a_old = A[0]
s = np.hstack((a_old, np.array(s[3:-2]) / d))
done = 0
while not done:
################ Mode #########################################
if i in td3_ep:
a = TD3.choose_action(s, 1)
if a[1] <= 0.9 and min_distance_ob <= 0.7 and abs(
theta_bt_uh_ob) < 1.3:
print(TGREEN + "Going to Crash!", ENDC)
a = A[1]
elif i in pureh_ep:
a = A[0]
s_, r, done, info = env.step(a)
sign_talker.publish(int(r))
if r < -400 and done:
done = 0
if i in td3_ep:
n_crash_s_TD3 += 1
elif i in pureh_ep:
n_crash_h += 1
print("n_crash_s_TD3:", n_crash_s_TD3)
print("n_crash_h:", n_crash_h)
print("i:", i)
print("n_d:", n_d)
min_distance_ob = s_[-2]
theta_bt_uh_ob = s_[-1]
a_old = a
s = np.hstack((a_old, np.array(s_[3:-2]) / d))
if done:
t = 0
n_d = n_d + 1
if n_d % 5 == 0:
instability = int(
input(
'How changeable is the situation (1: stable and straightforward -- 7: changing suddenly):'
))
variability = int(
input(
'How many variables are changing within the situation: (1: very few -- 7: large number):'
))
complexity = int(
input(
'How complicated is the situation (1: simple -- 7: complex):'
))
arousal = int(
input(
'How aroused are you in the situation (1: low degree of alertness -- 7: alert and ready for activity):'
))
spare = int(
input(
'How much mental capacity do you have to spare in the situation (1: Nothing to spare -- 7: sufficient):'
))
concentration = int(
input(
'How much are you concentrating on the situation (1: focus on only one thing -- 7: many aspect):'
))
attention = int(
input(
'How much is your attention divide in the situation (1: focus on only one thing -- 7: many aspect):'
))
quantity = int(
input(
'How much information have your gained about the situation (1: little -- 7: much):'
))
quality = int(
input(
'How good information have you been accessible and usable (1: poor -- 7: good):'
))
famlilarity = int(
input(
'How familar are you with the situation (1: New situation -- 7: a great deal of relevant experience):'
))
SA = quantity + quality + famlilarity - (
(instability + variability + complexity) -
(arousal + spare + concentration + attention))
if i in td3_ep:
SA_s_TD3 += SA
WL_s_TD3 += float(
input('Please input your workload (from TLX):'))
t_s_TD3 += t
n_s_TD3 += 1
if r > 500:
n_success_s_TD3 += 1
elif i in pureh_ep:
SA_h += SA
WL_h += float(
input('Please input your workload (from TLX):'))
t_h += t
n_h += 1
if r > 500:
n_success_h += 1
i = i + 1
break
np.savetxt(
'data.dat',
np.array([[
n_s_TD3, t_s_TD3, n_crash_s_TD3, n_success_s_TD3, SA_s_TD3,
WL_s_TD3
], [n_h, t_h, n_crash_h, n_success_h, SA_h, WL_h]]))
#,[n_s_ddpg,t_s_ddpg,n_crash_s_ddpg,n_success_s_ddpg, SA_s_ddpg, WL_s_ddpg]]))
########### shared_TD3: number of eps, total time, time of crash, time of success, situation awareness, workload ###########
########### human: number of eps, total time, time of crash, time of success, situation awareness, workload ###########
########### shared_TD3_ddpg: number of eps, total time, time of crash, time of success, situation awareness, workload ###########
os.system('cp -r data.dat ' + folder + '/')
class DQNRobotSolver():
def __init__(self,
environment_name,
n_observations,
n_actions,
n_episodes=1000,
n_win_ticks=195,
min_episodes=100,
max_env_steps=None,
gamma=1.0,
epsilon=1.0,
epsilon_min=0.01,
epsilon_log_decay=0.995,
alpha=0.01,
alpha_decay=0.01,
batch_size=64,
monitor=False,
quiet=False):
self.memory = deque(maxlen=100000)
# self.env = gym.make(environment_name)
self.env = StartOpenAI_ROS_Environment(environment_name)
if monitor:
self.env = gym.wrappers.Monitor(self.env,
'../data/cartpole-1',
force=True)
self.input_dim = n_observations
self.n_actions = n_actions
self.gamma = gamma
self.epsilon = epsilon
self.epsilon_min = epsilon_min
self.epsilon_decay = epsilon_log_decay
self.alpha = alpha
self.alpha_decay = alpha_decay
self.n_episodes = n_episodes
self.n_win_ticks = n_win_ticks
self.min_episodes = min_episodes
self.batch_size = batch_size
self.quiet = quiet
if max_env_steps is not None:
self.env._max_episode_steps = max_env_steps
# Init model
self.model = Sequential()
self.model.add(Dense(24, input_dim=self.input_dim, activation='tanh'))
self.model.add(Dense(48, activation='tanh'))
self.model.add(Dense(self.n_actions, activation='linear'))
self.model.compile(loss='mse',
optimizer=Adam(lr=self.alpha,
decay=self.alpha_decay))
def remember(self, state, action, reward, next_state, done):
self.memory.append((state, action, reward, next_state, done))
def choose_action(self, state, epsilon):
return self.env.action_space.sample() if (
np.random.random() <= epsilon) else np.argmax(
self.model.predict(state))
def get_epsilon(self, t):
return max(
self.epsilon_min,
min(self.epsilon, 1.0 - math.log10((t + 1) * self.epsilon_decay)))
def preprocess_state(self, state):
return np.reshape(state, [1, self.input_dim])
def replay(self, batch_size):
x_batch, y_batch = [], []
minibatch = random.sample(self.memory, min(len(self.memory),
batch_size))
for state, action, reward, next_state, done in minibatch:
y_target = self.model.predict(state)
y_target[0][
action] = reward if done else reward + self.gamma * np.max(
self.model.predict(next_state)[0])
x_batch.append(state[0])
y_batch.append(y_target[0])
self.model.fit(np.array(x_batch),
np.array(y_batch),
batch_size=len(x_batch),
verbose=0)
if self.epsilon > self.epsilon_min:
self.epsilon *= self.epsilon_decay
def run(self):
rate = rospy.Rate(30)
scores = deque(maxlen=100)
for e in range(self.n_episodes):
init_state = self.env.reset()
state = self.preprocess_state(init_state)
done = False
i = 0
while not done:
# openai_ros doesnt support render for the moment
#self.env.render()
action = self.choose_action(state, self.get_epsilon(e))
next_state, reward, done, _ = self.env.step(action)
next_state = self.preprocess_state(next_state)
self.remember(state, action, reward, next_state, done)
state = next_state
i += 1
scores.append(i)
mean_score = np.mean(scores)
if mean_score >= self.n_win_ticks and e >= min_episodes:
if not self.quiet:
print('Ran {} episodes. Solved after {} trials'.format(
e, e - min_episodes))
return e - min_episodes
if e % 1 == 0 and not self.quiet:
print(
'[Episode {}] - Mean survival time over last {} episodes was {} ticks.'
.format(e, min_episodes, mean_score))
self.replay(self.batch_size)
if not self.quiet: print('Did not solve after {} episodes'.format(e))
return e
def main():
# Initialize OpenAI_ROS ENV
LoadYamlFileParamsTest(rospackage_name="learning_ros",
rel_path_from_package_to_file="config",
yaml_file_name="aliengo_stand.yaml")
env = StartOpenAI_ROS_Environment('AliengoStand-v0')
time.sleep(3)
# Initialize PPO agent
Policy = Policy_net('policy', ob_space=3, act_space=8)
Old_Policy = Policy_net('old_policy', ob_space=3, act_space=8)
PPO = PPOTrain(Policy, Old_Policy, gamma=GAMMA)
saver = tf.train.Saver()
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
obs = env.reset()
reward = 0
success_num = 0
scores = []
for iteration in range(ITERATION):
observations = []
actions = []
v_preds = []
rewards = []
while not rospy.is_shutdown(): # until ros is not shutdown
# prepare to feed placeholder Policy.obs
obs = np.stack([obs]).astype(dtype=np.float32)
act, v_pred = Policy.act(obs=obs, stochastic=True)
#print('act: ',act, 'v_pred: ',v_pred )
act = np.asscalar(act)
v_pred = np.asscalar(v_pred)
observations.append(obs)
actions.append(act)
v_preds.append(v_pred)
rewards.append(reward)
#execute according to action
next_obs, reward, done, _ = env.step(act)
time.sleep(0.01)
if done:
# next state of terminate state has 0 state value
v_preds_next = v_preds[1:] + [0]
obs = env.reset()
break
else:
obs = next_obs
#scores store for visualization
scores.append(sum(rewards))
#if consectuvely 10 times has high reward end the training
if sum(rewards) >= 400:
success_num += 1
print("Succes number: " + str(success_num))
if success_num >= 5:
saver.save(sess, 'model_train/model_ppo.ckpt')
print('Clear!! Model saved.')
if success_num >= 10:
saver.save(sess, 'model_train/model_ppo.ckpt')
print('Finished! ')
break
else:
success_num = 0
gaes = PPO.get_gaes(rewards=rewards,
v_preds=v_preds,
v_preds_next=v_preds_next)
# convert list to numpy array for feeding tf.placeholder
observations = np.reshape(observations, [len(observations), 3])
actions = np.array(actions).astype(dtype=np.int32)
rewards = np.array(rewards).astype(dtype=np.float32)
v_preds_next = np.array(v_preds_next).astype(dtype=np.float32)
#calculate generative advantage estimator score
gaes = np.array(gaes).astype(dtype=np.float32)
gaes = (gaes - gaes.mean())
print('gaes', gaes)
#assign current policy params to previous policy params
PPO.assign_policy_parameters()
inp = [observations, actions, rewards, v_preds_next, gaes]
# PPO train
for epoch in range(4):
sample_indices = np.random.randint(
low=0, high=observations.shape[0],
size=64) # indices are in [low, high)
sampled_inp = [
np.take(a=a, indices=sample_indices, axis=0) for a in inp
] # sample training data
PPO.train(obs=sampled_inp[0],
actions=sampled_inp[1],
rewards=sampled_inp[2],
v_preds_next=sampled_inp[3],
gaes=sampled_inp[4])
plt.plot(scores)
plt.show()
env.stop()
