def __init__(self,
device,
state_size,
action_size,
buffer_size=10,
batch_size=10,
learning_rate=0.1,
discount_rate=0.99,
eps_decay=0.9,
tau=0.1,
steps_per_update=4):
self.device = device
self.state_size = state_size
self.action_size = action_size
self.q_network_control = QNetwork(state_size, action_size).to(device)
self.q_network_target = QNetwork(state_size, action_size).to(device)
self.optimizer = torch.optim.Adam(self.q_network_control.parameters(),
lr=learning_rate)
self.batch_size = batch_size
self.replay_buffer = ReplayBuffer(device, state_size, action_size,
buffer_size)
self.discount_rate = discount_rate
self.eps = 1.0
self.eps_decay = eps_decay
self.tau = tau
self.step_count = 0
self.steps_per_update = steps_per_update
def __init__(self, env, state_space, action_space, device, learning_rate, buffer_size, \
batch_size, gamma, in_channels, train_freq = 4, target_update_freq=1e4, is_ddqn = False):
self.env = env
self.state_space = state_space
self.action_space = action_space
self.QNetwork_local = QNetwork(in_channels, self.state_space, self.action_space.n, device = device).to(device)
self.QNetwork_local.init_weights()
self.QNetwork_target = QNetwork(in_channels, self.state_space,self.action_space.n , device = device).to(device)
self.QNetwork_target.load_state_dict(self.QNetwork_local.state_dict())
self.optimizer = torch.optim.RMSprop(self.QNetwork_local.parameters(), lr=learning_rate, alpha=0.95, eps=0.01, centered=True)
self.criterion = torch.nn.MSELoss()
self.memory = ReplayBuffer(capacity=int(buffer_size), batch_size=batch_size)
self.step_count = 0.
self.batch_size = batch_size
self.gamma = gamma
self.device = device
self.buffer_size = buffer_size
self.num_train_updates = 0
self.train_freq = train_freq
self.target_update_freq = target_update_freq
self.is_ddqn = is_ddqn
print('Agent initialized with memory size:{}'.format(buffer_size))
def main():
env = gym.make('CartPole-v1')
agent = QNetwork(env.observation_space, env.action_space)
frame = 0
while frame < MAX_NUM_FRAMES:
state = env.reset()
for t in range(MAX_EPISODE_DURATION):
env.render()
action = agent.act(state)
next_state, reward, done, info = env.step(action)
if done:
# done doesn't return a negative reward...
reward *= -1
frame = frame + 1
if done or frame == MAX_NUM_FRAMES:
break
state = next_state
env.close()
def _init_modules(self):
# Replay memory
self.replay_memory = ReplayMemory(history_len=self.num_frames,
capacity=self.capacity,
batch_size=self.batch_size,
input_scale=self.input_scale)
input_shape = self.feedback_size + (self.num_frames, )
# Q-network
self.q_network = QNetwork(input_shape=input_shape,
n_outputs=len(self.actions),
network_type=self.config['network_type'],
scope='q_network')
# Target network
self.target_network = QNetwork(
input_shape=input_shape,
n_outputs=len(self.actions),
network_type=self.config['network_type'],
scope='target_network')
# Optimizer
self.optimizer = Optimizer(config=self.config,
feedback_size=self.feedback_size,
q_network=self.q_network,
target_network=self.target_network,
replay_memory=self.replay_memory)
# Ops for updating target network
self.clone_op = self.target_network.get_clone_op(self.q_network)
# For tensorboard
self.t_score = tf.placeholder(dtype=tf.float32,
shape=[],
name='new_score')
tf.summary.scalar("score", self.t_score, collections=['dqn'])
self.summary_op = tf.summary.merge_all('dqn')
class Agent():
def __init__(self, state_size, action_size, fc_units, seed, lr,
buffer_size, batch_size, update_every):
self.state_size = state_size
self.action_size = action_size
self.seed = random.seed(seed)
self.update_every = update_every
self.qnetwork_local = QNetwork(state_size, action_size, seed,
fc_units).to(device)
self.qnetwork_target = QNetwork(state_size, action_size, seed,
fc_units).to(device)
self.optimizer = optim.Adam(self.qnetwork_local.parameters(), lr=lr)
self.scheduler = optim.lr_scheduler.StepLR(self.optimizer,
step_size=100,
gamma=0.5)
self.memory = ReplayBuffer(action_size, buffer_size, batch_size, seed)
self.t_step = 0
def step(self, state, action, reward, next_state, done, gamma, tau):
self.memory.add(state, action, reward, next_state, done)
self.t_step = (self.t_step + 1) % self.update_every
if (self.t_step == 0) and (len(self.memory) > self.memory.batch_size):
experiences = self.memory.sample()
self.learn(experiences, gamma, tau)
def act(self, state, eps):
state = torch.from_numpy(state).float().unsqueeze(0).to(device)
self.qnetwork_local.eval()
with torch.no_grad():
action_values = self.qnetwork_local(state)
self.qnetwork_local.train()
if random.random() > eps:
action = np.argmax(action_values.cpu().data.numpy())
else:
action = random.choice(np.arange(self.action_size))
return action
def learn(self, experiences, gamma, tau):
states, actions, rewards, next_states, dones = experiences
Q_targets_next = self.qnetwork_target(next_states).detach().max(
1)[0].unsqueeze(1)
Q_targets = rewards + gamma * Q_targets_next * (1 - dones)
Q_expected = self.qnetwork_local(states).gather(1, actions)
loss = F.mse_loss(Q_expected, Q_targets)
self.optimizer.zero_grad()
loss.backward()
self.optimizer.step()
self.soft_update(self.qnetwork_local, self.qnetwork_target, tau)
def soft_update(self, local_model, target_model, tau):
for target_param, local_param in zip(target_model.parameters(),
local_model.parameters()):
target_param.data.copy_(tau * local_param.data +
(1.0 - tau) * target_param.data)
def build_networks(self):
self.learning_network = QNetwork(
name=self.hyperparameters["agent_name"] + "_learning_q_network",
env=self.env,
hyperparameters=self.hyperparameters,
placeholders=self.placeholders,
sess=self.sess)
self.target_network = QNetwork(
name=self.hyperparameters["agent_name"] + "_target_q_network",
env=self.env,
hyperparameters=self.hyperparameters,
placeholders=self.placeholders,
sess=self.sess)
def __init__(self, state_size, action_size, seed):
self.state_size = state_size
self.action_size = action_size
self.seed = random.seed(seed)
# Q-Network
self.qnetwork_local = QNetwork(state_size, action_size,
seed).to(device)
self.qnetwork_target = QNetwork(state_size, action_size,
seed).to(device)
self.optimizer = optim.Adam(self.qnetwork_local.parameters(), lr=LR)
# Replay memory
self.memory = ReplayBuffer(action_size, BUFFER_SIZE, BATCH_SIZE, seed)
# Initialize time step (for updating every UPDATE_EVERY steps)
self.t_step = 0
def __init__(self, state_size, action_size, fc_units, seed, lr,
buffer_size, batch_size, update_every):
self.state_size = state_size
self.action_size = action_size
self.seed = random.seed(seed)
self.update_every = update_every
self.qnetwork_local = QNetwork(state_size, action_size, seed,
fc_units).to(device)
self.qnetwork_target = QNetwork(state_size, action_size, seed,
fc_units).to(device)
self.optimizer = optim.Adam(self.qnetwork_local.parameters(), lr=lr)
self.scheduler = optim.lr_scheduler.StepLR(self.optimizer,
step_size=100,
gamma=0.5)
self.memory = ReplayBuffer(action_size, buffer_size, batch_size, seed)
self.t_step = 0
def __init__(self, env, hidden_units = None, network_LR=0.01, batch_size=1024, update_every=5, gamma=0.95):
"""
Creates a DQN agent.
:param env: game environment.
:type env: Class Snake_Env().
:param hidden_units: number of neurons in each layer.
:type hidden_units: tupple with dimension (1, 3).
:param network_LR: learning rate of the action-value neural network.
:type network_LR: float.
:param batch_size: size of the minibatch taken from the replay buffer.
:type batch_size: int.
:param update_every: number of iterations for updating the target qnetwork.
:type update_every: int
:param gamma: discount factor.
:type gamma: float.
"""
self.env = env
self.BATCH_SIZE = batch_size
self.GAMMA = gamma
self.NETWORK_LR = network_LR
self.MEMORY_CAPACITY = int(1e5)
self.ACTION_SIZE = env.ACTION_SPACE
self.HIDDEN_UNITS = hidden_units
self.UPDATE_EVERY = update_every
self.qnetwork_local = QNetwork(input_shape = self.env.STATE_SPACE,
hidden_units = self.HIDDEN_UNITS,
output_size = self.ACTION_SIZE,
learning_rate = self.NETWORK_LR)
self.qnetwork_target = QNetwork(input_shape = self.env.STATE_SPACE,
hidden_units = self.HIDDEN_UNITS,
output_size = self.ACTION_SIZE,
learning_rate = self.NETWORK_LR)
self.memory = ReplayMemory(self.MEMORY_CAPACITY, self.BATCH_SIZE)
#Temp variable
self.t = 0
def __init__(self, state_size, action_size, seed, training, pixels, lr=LR):
self.state_size = state_size
self.action_size = action_size
self.seed = random.seed(seed)
self.t_step = 0.
self.pixels = pixels
if pixels is False:
from q_network import QNetwork
else:
from q_network_cnn import QNetwork
print('loaded cnn network')
self.loader = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
])
self.QN_local = QNetwork(state_size, action_size, seed,
training).to(device)
self.QN_target = QNetwork(state_size, action_size, seed,
training).to(device)
self.optimizer = optim.Adam(self.QN_local.parameters(), lr=lr)
self.memory = ReplayBuffer(action_size, BUFFER_SIZE, BATCH_SIZE, seed,
device) # TODO
def main(env_name, optim, hiddens, eps_decay, memory_length, epochs,
batch_size):
env = gym.make(env_name)
if hiddens:
hidden_sizes = map(int, hiddens.split(','))
else:
hidden_sizes = []
q_network = QNetwork(inps=env.observation_space.shape[0],
outs=env.action_space.n,
hidden_sizes=hidden_sizes,
str_optim=optim)
policy = EpsilonGreedyPolicy(start_eps=1.0,
eps_decay=eps_decay,
action_values=q_network.get_action_values,
n_actions=env.action_space.n)
memory = Memory(max_length=memory_length)
Transition = namedtuple(
'Transition', ["state", "action", "reward", "state_next", "done"])
for epoch in range(epochs):
state = env.reset()
epoch_reward = 0
for step in range(env.spec.max_episode_steps):
action = policy.act(state)
next_state, reward, done, _ = env.step(action)
memory.store(Transition(state, action, reward, next_state, done))
try:
q_network.fit_transitions(memory.random_sample(batch_size))
except MemTooSmallError: # memory not filled yet
pass
policy.update_epsilon()
epoch_reward += reward
state = next_state
if done: break
print 'Episode {}/{} | Total reward: {} | Epsilon: {:.4f}'\
.format(epoch, epochs, epoch_reward, policy.epsilon)
return
def __init__(self,
env,
hidden_units=None,
network_LR=0.001,
batch_size=64,
update_every=4,
gamma=1.0):
self.env = env
self.BATCH_SIZE = batch_size
self.GAMMA = gamma
self.NETWORK_LR = network_LR
self.MEMORY_CAPACITY = int(1e5) #this is pythonic
self.nA = env.ACTION_SPACE #number of actions agent can perform
self.HIDDEN_UNITS = hidden_units
self.UPDATE_EVERY = update_every
#let's give it some brains
self.qnetwork_local = QNetwork(input_shape=self.env.STATE_SPACE,
hidden_units=self.HIDDEN_UNITS,
output_size=self.nA,
learning_rate=self.NETWORK_LR)
print(self.qnetwork_local.model.summary())
#I call the target network as the PC
# Where our agent stores all the concrete and important stuff
self.qnetwork_target = QNetwork(input_shape=self.env.STATE_SPACE,
hidden_units=self.HIDDEN_UNITS,
output_size=self.nA,
learning_rate=self.NETWORK_LR)
#and the memory of course
self.memory = ReplayMemory(self.MEMORY_CAPACITY, self.BATCH_SIZE)
#handy temp variable
self.t = 0
def main():
env = gym.make('CartPole-v1')
replay_memory = ReplayMemory()
agent = QNetwork(env.observation_space, env.action_space)
get_training_batch = make_training_transformer(env.observation_space.shape,
agent)
frame = 0
acc_loss = 0
acc_state_value = 0
while frame < MAX_NUM_FRAMES:
state = env.reset()
for t in range(MAX_EPISODE_DURATION):
if take_random_action(frame):
action = env.action_space.sample() # pick random action
else:
action = agent.act(state)
next_state, reward, done, info = env.step(action)
if done:
# on done doesn't return a negative reward...
reward *= -1
experience = (state, action, reward, next_state, done)
replay_memory.append(experience)
frame += 1
experience_samples = replay_memory.sample(BATCH_SIZE)
state_batch, qs_batch = get_training_batch(experience_samples)
acc_state_value += np.mean(qs_batch)
loss = agent.training_step(state_batch, qs_batch)
acc_loss += loss
if frame % FRAMES_PER_SAVE == 0:
model_filename = f"ckpt-loss={loss:.4f}"
agent.save_model(model_filename)
if frame % FRAMES_PER_PRINT == 0:
print(f"Frame: {frame}")
avg_loss = acc_loss / FRAMES_PER_PRINT
avg_state_value = acc_state_value / FRAMES_PER_PRINT
print(
f"avg loss: {avg_loss:.4f}; avg value: {avg_state_value:.2f}"
)
acc_loss = 0
acc_state_value = 0
if done or frame == MAX_NUM_FRAMES:
break
state = next_state
env.close()
def __init__(self, params):
action_size = params['action_size']
state_size = params['state_size']
buf_params = params['buf_params']
nn_params = params['nn_params']
nn_params['l1'][0] = state_size
nn_params['l5'][1] = action_size
self.__learning_mode = params['learning_mode']
if self.__learning_mode['DuelingDDQN']:
self.__qnetwork_local = DuelingQNetwork(nn_params).to(device)
self.__qnetwork_target = DuelingQNetwork(nn_params).to(device)
else:
self.__qnetwork_local = QNetwork(nn_params).to(device)
self.__qnetwork_target = QNetwork(nn_params).to(device)
self.__action_size = action_size
self.__state_size = state_size
self.__memory = ReplayBuffer(buf_params)
self.__t = 0
self.eps = params['eps_initial']
self.gamma = params['gamma']
self.learning_rate = params['learning_rate']
self.update_period = params['update_period']
self.a = params['a']
self.b = params['b']
self.e = params['e']
self.tau = params['tau']
self.__optimiser = optim.Adam(self.__qnetwork_local.parameters(),
self.learning_rate)
# other parameters
self.agent_loss = 0.0
def main():
"""
Main function for training the DQN network in learning how to accomplish autonomous landing.
"""
# ATTENTION: If you want to restore files from a previous simulation you
# must pass valid values for these variables:
policy_weights_path = '/home/pulver/Desktop/episode_113250/policy/policy_checkpoint.ckp'
summary_folder = "" # if empty the summary is written in ./log/ + current time
screen_width = 84 # original is 160
screen_height = 84 # original is 210
images_stack_size = 4
# Use only the first 5 actions for this simulation
# action_list = ['left', 'right', 'forward', 'backward', 'stop', 'descend']
# 0 (left, 1 (right), 2 (forward), 3 (backward), 4 (stop), 5 (descend)
tot_actions = 6
batch_size = 32 # size of the experience batch
tot_steps = 1000 # finite-horizont simulation
# 10x10^6 high number since training can be stopped at every time
steps_per_episodes = 40 # expressed in number step
noop_time = 2.0 # pause in seconds between actions
timer_total_start = time.time()
rospy.init_node("DRLTestNode")
rospy.loginfo("----- DRL Test Node -----")
# Create a subscriber fot the greyscale image
rospy.Subscriber("/drl/grey_camera", ROSImage, image_callback) # TODO
# restore default
# rospy.Subscriber("/quadrotor/ardrone/bottom/ardrone/bottom/image_raw", ROSImage, image_callback, queue_size=30) # Store the last 30 messages
# before discarding them
images_stack_size = 4
r = rospy.Rate(10) # 10hz
# Init session and networks
sess = tf.Session()
if (summary_folder == ""):
tf_summary_writer = tf.summary.FileWriter(
'./log/' + str(datetime.datetime.now().time()), sess.graph)
else:
tf_summary_writer = tf.summary.FileWriter(summary_folder, sess.graph)
policy_network = QNetwork(sess,
tot_actions=tot_actions,
image_shape=(screen_width, screen_height,
images_stack_size),
batch_size=batch_size,
network_name="policy_net")
init = tf.global_variables_initializer()
sess.run(init)
if (policy_weights_path != ""):
print("Loading weights from memory: " + str(policy_weights_path))
policy_network.load_weights(policy_weights_path)
else:
print("The networks path are empty. Choose appropriate weights!")
sys.exit()
##########################################################################
## START HERE ##
##########################################################################
timer_start = time.time()
actual_time = rospy.get_rostime()
rospy_start_time = actual_time.secs + actual_time.nsecs / 1000000000.0
frame_episode = 0
cumulated_reward = 0
send_action("takeoff")
# 1-Accumulate the first state
send_action("stop")
rospy.sleep(1.0)
# acquire image from bottomcamera
# get_image()
state = _last_image
# create a stack of X images
image_t = np.stack([state] * images_stack_size, axis=2)
for step in range(tot_steps):
# 2- Get the action following epsilon-greedy or through policy network.
# Here image_t is always ready and it can be given as input to
# the network
action_distribution = policy_network.return_action_distribution(
input_data=np.reshape(image_t, (1, 84, 84, images_stack_size)),
softmax=False)
# print ""
# print action_distribution
action = np.argmax(action_distribution)
action = convert_action_int_to_str(action)
# print action
send_action(action)
rospy.sleep(noop_time)
# 3- Get the state_t1 repeating the action obtained at step-2 and add everything
# in the replay buffer. Then set state_t = state_t1
# get_image()
image_t1 = _last_image
send_action("stop")
# If the action taken is to land and the UAV is inside the landing
# BB, done will be calculated accordingly
# NOTE: in the real implementation there's no way to get done and reward
# done_reward = get_done_reward()
# reward = done_reward.reward
# done = done_reward.done
image_t1 = np.expand_dims(image_t1, 2)
# stack the images
image_t1 = np.append(image_t[:, :, 1:], image_t1, axis=2)
frame_episode += 1
image_t = image_t1
# cumulated_reward += reward
# At every step check if more than 30 seconds from the start passed.
# In that case, set done = True and end the episode
timer_stop = time.time()
# Stop the episode if the number of frame is more than a threshold
if frame_episode >= steps_per_episodes:
done = True
# When the episode is done
if done:
timer_stop = time.time()
actual_time = rospy.get_rostime()
rospy_stop_time = actual_time.secs + actual_time.nsecs / 1000000000.0
rospy_time_elapsed = rospy_stop_time - rospy_start_time
print("Time episode: " + str(timer_stop - timer_start) +
" seconds")
print("Ros time episode: " + str(rospy_time_elapsed) + " seconds")
# if cumulated_reward >= 0:
# rospy.logwarn("Positive reward obtained!")
# print("Cumulated reward: " + str(cumulated_reward))
print("Episode finished after {} timesteps".format(step + 1))
sys.stdout.flush()
break
class Agent():
def __init__(self, state_size, action_size, seed, training, pixels, lr=LR):
self.state_size = state_size
self.action_size = action_size
self.seed = random.seed(seed)
self.t_step = 0.
self.pixels = pixels
if pixels is False:
from q_network import QNetwork
else:
from q_network_cnn import QNetwork
print('loaded cnn network')
self.loader = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
])
self.QN_local = QNetwork(state_size, action_size, seed,
training).to(device)
self.QN_target = QNetwork(state_size, action_size, seed,
training).to(device)
self.optimizer = optim.Adam(self.QN_local.parameters(), lr=lr)
self.memory = ReplayBuffer(action_size, BUFFER_SIZE, BATCH_SIZE, seed,
device) # TODO
def act(self, state, eps):
# if self.pixels is True:
#state = Variable(torch.from_numpy(state).float().to(device).view(state.shape[0],3,32,32))
if not self.pixels:
state = torch.from_numpy(state).float().unsqueeze(0).to(device)
self.QN_local.eval()
if torch.no_grad():
action_values = self.QN_local(state)
self.QN_local.train()
if random.random() > eps:
return int(np.argmax(action_values.cpu().data.numpy()))
else:
return int(random.choice(np.arange(self.action_size)))
def step(self, state, action, reward, next_state, done, stack_size):
self.memory.add(state, action, reward, next_state, done)
self.t_step = (self.t_step + 1) % UPDATE_RATE
if self.t_step == 0 and len(self.memory) > BATCH_SIZE:
samples = self.memory.sample()
self.learn(samples, GAMMA, stack_size)
def learn(self, experiences, gamma, stack_size):
states, actions, rewards, next_states, dones = experiences
if self.pixels:
next_states = Variable(
next_states
) #next_states.view(next_states.shape[0],stack_size,3, stack_size,32,32))
states = Variable(states) #states.view(states.shape[0],3,64,64))
# else:
#todo bring back the old version stuff here
_target = self.QN_target(next_states).detach().max(1)[0].unsqueeze(1)
action_values_target = rewards + gamma * _target * (1 - dones)
action_values_expected = self.QN_local(states).gather(1, actions)
loss = F.mse_loss(action_values_expected, action_values_target)
self.optimizer.zero_grad()
loss.backward()
self.optimizer.step()
# update target Qnetwork
for target_param, local_param in zip(self.QN_target.parameters(),
self.QN_local.parameters()):
target_param.data.copy_(TAU * local_param.data +
(1.0 - TAU) * target_param.data)
def main():
parser = argparse.ArgumentParser(
'a program to train or run a deep q-learning agent')
parser.add_argument("game", type=str, help="name of game to play")
parser.add_argument("agent_type",
type=str,
help="name of learning/acting technique used")
parser.add_argument("agent_name",
type=str,
help="unique name of this agent instance")
parser.add_argument("--rom_path",
type=str,
help="path to directory containing atari game roms",
default='../roms')
parser.add_argument(
"--watch",
help=
"if true, a pretrained model with the specified name is loaded and tested with the game screen displayed",
action='store_true')
parser.add_argument("--epochs",
type=int,
help="number of epochs",
default=200)
parser.add_argument("--epoch_length",
type=int,
help="number of steps in an epoch",
default=250000)
parser.add_argument("--test_steps",
type=int,
help="max number of steps per test",
default=125000)
parser.add_argument("--test_steps_hardcap",
type=int,
help="absolute max number of steps per test",
default=135000)
parser.add_argument("--test_episodes",
type=int,
help="max number of episodes per test",
default=30)
parser.add_argument("--history_length",
type=int,
help="number of frames in a state",
default=4)
parser.add_argument("--training_frequency",
type=int,
help="number of steps run before training",
default=4)
parser.add_argument(
"--random_exploration_length",
type=int,
help=
"number of randomly-generated experiences to initially fill experience memory",
default=50000)
parser.add_argument("--initial_exploration_rate",
type=float,
help="initial exploration rate",
default=1.0)
parser.add_argument("--final_exploration_rate",
type=float,
help="final exploration rate from linear annealing",
default=0.1)
parser.add_argument(
"--final_exploration_frame",
type=int,
help="frame at which the final exploration rate is reached",
default=1000000)
parser.add_argument("--test_exploration_rate",
type=float,
help="exploration rate while testing",
default=0.05)
parser.add_argument("--frame_skip",
type=int,
help="number of frames to repeat chosen action",
default=4)
parser.add_argument("--screen_dims",
type=tuple,
help="dimensions to resize frames",
default=(84, 84))
# used for stochasticity and to help prevent overfitting.
# Must be greater than frame_skip * (observation_length -1) + buffer_length - 1
parser.add_argument("--max_start_wait",
type=int,
help="max number of frames to wait for initial state",
default=60)
# buffer_length = 1 prevents blending
parser.add_argument("--buffer_length",
type=int,
help="length of buffer to blend frames",
default=2)
parser.add_argument("--blend_method",
type=str,
help="method used to blend frames",
choices=('max'),
default='max')
parser.add_argument("--reward_processing",
type=str,
help="method to process rewards",
choices=('clip', 'none'),
default='clip')
# must set network_architecture to custom in order use custom architecture
parser.add_argument(
"--conv_kernel_shapes",
type=tuple,
help=
"shapes of convnet kernels: ((height, width, in_channels, out_channels), (next layer))"
)
# must have same length as conv_kernel_shapes
parser.add_argument(
"--conv_strides",
type=tuple,
help="connvet strides: ((1, height, width, 1), (next layer))")
# currently, you must have at least one dense layer
parser.add_argument(
"--dense_layer_shapes",
type=tuple,
help="shapes of dense layers: ((in_size, out_size), (next layer))")
parser.add_argument("--discount_factor",
type=float,
help="constant to discount future rewards",
default=0.99)
parser.add_argument("--learning_rate",
type=float,
help="constant to scale parameter updates",
default=0.00025)
parser.add_argument("--optimizer",
type=str,
help="optimization method for network",
choices=('rmsprop', 'graves_rmsprop'),
default='rmsprop')
parser.add_argument("--rmsprop_decay",
type=float,
help="decay constant for moving average in rmsprop",
default=0.95)
parser.add_argument("--rmsprop_epsilon",
type=int,
help="constant to stabilize rmsprop",
default=0.01)
# set error_clipping to less than 0 to disable
parser.add_argument(
"--error_clipping",
type=str,
help="constant at which td-error becomes linear instead of quadratic",
default=1.0)
# set gradient clipping to 0 or less to disable. Currently only works with graves_rmsprop.
parser.add_argument("--gradient_clip",
type=str,
help="clip gradients to have the provided L2-norm",
default=0)
parser.add_argument("--target_update_frequency",
type=int,
help="number of steps between target network updates",
default=10000)
parser.add_argument(
"--memory_capacity",
type=int,
help="max number of experiences to store in experience memory",
default=1000000)
parser.add_argument(
"--batch_size",
type=int,
help="number of transitions sampled from memory during learning",
default=32)
# must set to custom in order to specify custom architecture
parser.add_argument("--network_architecture",
type=str,
help="name of prespecified network architecture",
choices=("deepmind_nips", "deepmind_nature, custom"),
default="deepmind_nature")
parser.add_argument("--recording_frequency",
type=int,
help="number of steps before tensorboard recording",
default=50000)
parser.add_argument("--saving_threshold",
type=int,
help="min score threshold for saving model.",
default=0)
parser.add_argument("--parallel",
help="parallelize acting and learning",
action='store_true')
parser.add_argument(
"--double_dqn",
help="use double q-learning algorithm in error target calculation",
action='store_true')
args = parser.parse_args()
if args.network_architecture == 'deepmind_nature':
args.conv_kernel_shapes = [[8, 8, 4, 32], [4, 4, 32, 64],
[3, 3, 64, 64]]
args.conv_strides = [[1, 4, 4, 1], [1, 2, 2, 1], [1, 1, 1, 1]]
args.dense_layer_shapes = [[3136, 512]]
elif args.network_architecture == 'deepmind_nips':
args.conv_kernel_shapes = [[8, 8, 4, 16], [4, 4, 16, 32]]
args.conv_strides = [[1, 4, 4, 1], [1, 2, 2, 1]]
args.dense_layer_shapes = [[2592, 256]]
if not args.watch:
train_stats = RecordStats(args, False)
test_stats = RecordStats(args, True)
training_emulator = AtariEmulator(args)
testing_emulator = AtariEmulator(args)
num_actions = len(training_emulator.get_possible_actions())
experience_memory = ExperienceMemory(args, num_actions)
q_network = None
agent = None
if args.parallel:
q_network = ParallelQNetwork(args, num_actions)
agent = ParallelDQNAgent(args, q_network, training_emulator,
experience_memory, num_actions,
train_stats)
else:
q_network = QNetwork(args, num_actions)
agent = DQNAgent(args, q_network, training_emulator,
experience_memory, num_actions, train_stats)
experiment.run_experiment(args, agent, testing_emulator, test_stats)
else:
testing_emulator = AtariEmulator(args)
num_actions = len(testing_emulator.get_possible_actions())
q_network = QNetwork(args, num_actions)
agent = DQNAgent(args, q_network, None, None, num_actions, None)
experiment.evaluate_agent(args, agent, testing_emulator, None)
class Agent:
def __init__(self, env, state_space, action_space, device, learning_rate, buffer_size, \
batch_size, gamma, in_channels, train_freq = 4, target_update_freq=1e4, is_ddqn = False):
self.env = env
self.state_space = state_space
self.action_space = action_space
self.QNetwork_local = QNetwork(in_channels, self.state_space, self.action_space.n, device = device).to(device)
self.QNetwork_local.init_weights()
self.QNetwork_target = QNetwork(in_channels, self.state_space,self.action_space.n , device = device).to(device)
self.QNetwork_target.load_state_dict(self.QNetwork_local.state_dict())
self.optimizer = torch.optim.RMSprop(self.QNetwork_local.parameters(), lr=learning_rate, alpha=0.95, eps=0.01, centered=True)
self.criterion = torch.nn.MSELoss()
self.memory = ReplayBuffer(capacity=int(buffer_size), batch_size=batch_size)
self.step_count = 0.
self.batch_size = batch_size
self.gamma = gamma
self.device = device
self.buffer_size = buffer_size
self.num_train_updates = 0
self.train_freq = train_freq
self.target_update_freq = target_update_freq
self.is_ddqn = is_ddqn
print('Agent initialized with memory size:{}'.format(buffer_size))
def act(self, state, epsilon):
if random.random() > epsilon:
#switch to evaluation mode, evaluate state, switch back to train mode
self.QNetwork_local.eval()
if torch.no_grad():
actions = self.QNetwork_local(state)
self.QNetwork_local.train()
actions_np = actions.data.cpu().numpy()[0]
best_action_idx = int(np.argmax(actions_np))
return best_action_idx
else:
rand_action = self.action_space.sample()
return rand_action
def step(self, state, action, reward, next_state , done, add_memory=True):
#TODO calculate priority here?
if add_memory:
priority = 1.
reward_clip = np.sign(reward)
self.memory.add(state=state, action=action, next_state=next_state, reward=reward_clip, done=done, priority=priority)
self.step_count = (self.step_count+1) % self.train_freq #self.update_rate
#if self.step_count == 0 and len(self.memory) >= self.batch_size:
self.network_is_updated = False
if self.step_count == 0 and len(self.memory) == self.buffer_size:
samples = self.memory.random_sample(self.device)
self.learn(samples)
self.num_train_updates +=1
self.network_is_updated = True
def learn(self, samples):
states, actions, rewards, next_states, dones = samples
if self.is_ddqn is True:
# DDQN: find max action using local network & gather the values of actions from target network
next_actions = torch.argmax(self.QNetwork_local(next_states).detach(), dim=1).unsqueeze(1)
q_target_next = self.QNetwork_target(next_states).gather(1,next_actions)
else:
# DQN: find the max action from target network
q_target_next = self.QNetwork_target(next_states).detach().max(1)[0].unsqueeze(1)
# expected actions
q_local_current = self.QNetwork_local(states).gather(1,actions)
self.optimizer.zero_grad() #cleans up previous values
# TD Error
TD_target = rewards + (self.gamma*q_target_next * (1-dones))
TD_error = self.criterion(q_local_current, TD_target)
TD_error.backward()
torch.nn.utils.clip_grad_norm_(self.QNetwork_local.parameters(), 5.)
self.optimizer.step()
if (self.num_train_updates/self.train_freq) % self.target_update_freq == 0:
self.QNetwork_target.load_state_dict(self.QNetwork_local.state_dict())
index = x.index(1)
y = [0 for _ in range(index)] + [1 for _ in range(len(x) - index)]
ys.append(y)
if num_batches > batch_size:
remainder = num_batches % batch_size
while remainder != 0:
xs.append([0 for _ in range(seq_length)])
ys.append([0 for _ in range(seq_length)])
remainder -= 1
xs = torch.Tensor(xs).unsqueeze(2).to(device)
ys = torch.Tensor(ys).unsqueeze(2).to(device)
print('x: {}'.format(xs[:, :, 0]))
print('y: {}'.format(ys[:, :, 0]))
return xs, ys
q = QNetwork(input_size, hidden_size, seq_length).to(device)
p = PNetwork(input_size, hidden_size, seq_length).to(device)
params = list(q.parameters()) + list(p.parameters())
optimizer = torch.optim.Adagrad(params, lr=lr)
def train(xs, ys):
q.train()
p.train()
train_loss = 0
for epoch in range(num_epochs):
for b in range(0, num_batches, batch_size):
z = q.sample(xs[b:b+batch_size], ys[b:b+batch_size], 0)
z = z.detach()
# print(z.size())
def deep_qlearning(env, nframes, discount_factor, N, C, mini_batch_size,
replay_start_size, sgd_update_frequency,
initial_exploration, final_exploration,
final_exploration_frame, lr, alpha, m):
"""
Input:
- env: environment
- nframes: # of frames to train on
- discount_factor (gamma): how much to discount future rewards
- N: replay memory size
- C: number of steps before updating Q target network
- mini_batch_size: mini batch size
- replay_start_size: minimum size of replay memory before learning starts
- sgd_update_frequency: number of action selections in between consecutive
mini batch SGD updates
- initial_exploration: initial epsilon value
- final_exploration: final epsilon value
- final_exploration_frame: number of frames over which the epsilon is
annealed to its final value
- lr: learning rate used by RMSprop
- alpha: alpha value used by RMSprop
- m: number of consecutive frames to stack for input to Q network
Output:
- Q: trained Q-network
"""
n_actions = env.action_space.n
Q = QNetwork(n_actions)
Q_target = deepcopy(Q)
Q_target.eval()
transform = T.Compose([T.ToTensor()])
optimizer = optim.RMSprop(Q.parameters(), lr=lr, alpha=alpha)
criterion = nn.MSELoss()
D = deque(maxlen=N) # replay memory
last_Q_target_update = 0
frames_count = 0
last_sgd_update = 0
episodes_count = 0
episode_rewards = []
while True:
frame_sequence = initialize_frame_sequence(env, m)
state = transform(np.stack(frame_sequence, axis=2))
episode_reward = 0
done = False
while not done:
epsilon = annealed_epsilon(initial_exploration, final_exploration,
final_exploration_frame, frames_count)
action = get_epsilon_greedy_action(Q, state.unsqueeze(0), epsilon,
n_actions)
frame, reward, done, _ = env.step(action.item())
reward = torch.tensor([reward])
episode_reward += reward.item()
if done:
next_state = None
episode_rewards.append(episode_reward)
else:
frame_sequence.append(preprocess_frame(frame))
next_state = transform(np.stack(frame_sequence, axis=2))
D.append((state, action, reward, next_state))
state = next_state
if len(D) < replay_start_size:
continue
last_sgd_update += 1
if last_sgd_update < sgd_update_frequency:
continue
last_sgd_update = 0
sgd_update(Q, Q_target, D, mini_batch_size, discount_factor,
optimizer, criterion)
last_Q_target_update += 1
frames_count += mini_batch_size
if last_Q_target_update % C == 0:
Q_target = deepcopy(Q)
Q_target.eval()
if frames_count >= nframes:
return Q, episode_rewards
episodes_count += 1
if episodes_count % 100 == 0:
save_stuff(Q, episode_rewards)
print(f'episodes completed = {episodes_count},',
f'frames processed = {frames_count}')
from agents.deep_q import DeepQLearning
from environments.snake import SnakeVisual
from q_network import QNetwork
env = SnakeVisual(grid_size=[8, 8], render=True, render_freq=1)
actions = env.valid_actions()
size = np.shape(env.reset().state)
nn = ks.models.Sequential()
nn.add(ks.layers.Conv2D(filters=16, kernel_size=(3, 3), activation='sigmoid', input_shape=size))
nn.add(ks.layers.Conv2D(filters=24, kernel_size=(3, 3), activation='sigmoid'))
nn.add(ks.layers.Conv2D(filters=32, kernel_size=(3, 3), activation='sigmoid'))
nn.add(ks.layers.Flatten())
nn.add(ks.layers.Dense(units=16, activation='sigmoid'))
nn.add(ks.layers.Dense(units=3, activation='linear'))
nn.compile(optimizer=ks.optimizers.Adam(lr=0.0001), loss='mse')
print(nn.summary())
def normalize_state(s):
return np.reshape(s.state, newshape=(1,) + size)
dqn = QNetwork(nn, actions, normalize_state)
dql = DeepQLearning(env, dqn)
q = dql.learn()
class Agent:
def __init__(self,
device,
state_size,
action_size,
buffer_size=10,
batch_size=10,
learning_rate=0.1,
discount_rate=0.99,
eps_decay=0.9,
tau=0.1,
steps_per_update=4):
self.device = device
self.state_size = state_size
self.action_size = action_size
self.q_network_control = QNetwork(state_size, action_size).to(device)
self.q_network_target = QNetwork(state_size, action_size).to(device)
self.optimizer = torch.optim.Adam(self.q_network_control.parameters(),
lr=learning_rate)
self.batch_size = batch_size
self.replay_buffer = ReplayBuffer(device, state_size, action_size,
buffer_size)
self.discount_rate = discount_rate
self.eps = 1.0
self.eps_decay = eps_decay
self.tau = tau
self.step_count = 0
self.steps_per_update = steps_per_update
def policy(self, state):
state = torch.from_numpy(state).float().unsqueeze(0).to(self.device)
return self.epsilon_greedy_policy(self.eps, state)
def epsilon_greedy_policy(self, eps, state):
self.q_network_control.eval()
with torch.no_grad():
action_values = self.q_network_control(state)
self.q_network_control.train()
if random.random() > eps:
greedy_choice = np.argmax(action_values.cpu().data.numpy())
return greedy_choice
else:
return random.choice(np.arange(self.action_size))
def step(self, state, action, reward, next_state, done):
p = self.calculate_p(state, action, reward, next_state, done)
self.replay_buffer.add(state, action, reward, next_state, done, p)
if self.step_count % self.steps_per_update == 0:
self.learn()
self.step_count += 1
def learn(self):
if len(self.replay_buffer) < self.batch_size:
return
states, actions, rewards, next_states, dones, p = \
self.replay_buffer.sample(self.batch_size)
error = self.bellman_eqn_error(states, actions, rewards, next_states,
dones)
importance_scaling = (self.replay_buffer.buffer_size * p)**-1
loss = (importance_scaling * (error**2)).sum() / self.batch_size
self.optimizer.zero_grad()
loss.backward()
self.optimizer.step()
self.update_target()
def bellman_eqn_error(self, states, actions, rewards, next_states, dones):
"""Double DQN error - use the control network to get the best action
and apply the target network to it to get the target reward which is
used for the bellman eqn error.
"""
self.q_network_control.eval()
with torch.no_grad():
a_max = self.q_network_control(next_states).argmax(1).unsqueeze(1)
target_action_values = self.q_network_target(next_states).gather(
1, a_max)
target_rewards = rewards + self.discount_rate * (1 - dones) \
* target_action_values
self.q_network_control.train()
current_rewards = self.q_network_control(states).gather(1, actions)
error = current_rewards - target_rewards
return error
def calculate_p(self, state, action, reward, next_state, done):
next_state = torch.from_numpy(next_state[np.newaxis, :]).float().to(
self.device)
state = torch.from_numpy(state[np.newaxis, :]).float().to(self.device)
action = torch.from_numpy(np.array([[action]])).long().to(self.device)
reward = torch.from_numpy(np.array([reward])).float().to(self.device)
done = torch.from_numpy(np.array([[done]], dtype=np.uint8)).float().to(
self.device)
return abs(
self.bellman_eqn_error(state, action, reward, next_state,
done)) + 1e-3
def update_target(self):
for target_param, control_param in zip(
self.q_network_target.parameters(),
self.q_network_control.parameters()):
target_param.data.copy_(self.tau * control_param.data +
(1.0 - self.tau) * target_param.data)
def end_of_episode(self):
self.eps *= self.eps_decay
self.step_count = 0
def save(self, path):
torch.save(self.q_network_control.state_dict(), path)
def restore(self, path):
self.q_network_control.load_state_dict(torch.load(path))
if __name__ == '__main__':
import keras as ks
import numpy as np
from agents.deep_q import DeepQLearning
from environments.cartpole import CartPole
from q_network import QNetwork
nn = ks.models.Sequential()
nn.add(ks.layers.Dense(32, activation='sigmoid', input_shape=(4, )))
nn.add(ks.layers.Dense(32, activation='sigmoid'))
nn.add(ks.layers.Dense(2, activation='linear'))
nn.compile(optimizer=ks.optimizers.Adam(lr=0.001), loss='mse')
env = CartPole(render=True)
actions = env.valid_actions()
dqn = QNetwork(nn, actions, lambda x: np.reshape(x.state, newshape=(1, 4)))
dql = DeepQLearning(env, dqn)
q = dql.learn()
def main():
"""
Main function for training the DQN network in learning how to accomplish autonomous landing.
"""
# ATTENTION: If you want to restore files from a previous simulation you
# must pass valid values for these variables:
policy_weights_path = '/home/pulver/Desktop/DQN-single/episode_36000/policy/policy_checkpoint.ckp'
ground_list = [
"asphalt11_small", "asphalt12_small", "asphalt13_small",
"brick11_small", "brick12_small", "brick13_small", "grass11_small",
"grass12_small", "grass13_small", "pavement11_small",
"pavement12_small", "pavement13_small", "sand11_small", "sand12_small",
"sand13_small", "snow11_small", "snow12_small", "snow13_small",
"soil11_small", "soil12_small", "soil13_small"
]
summary_folder = "" # if empty the summary is written in ./log/ + current time
start_episode = 0 # change to last episode number
frame_counter = 0 # change to last number of frames
screen_width = 84 # original is 160
screen_height = 84 # original is 210
images_stack_size = 4
# Use only the first 5 actions for this simulation
# 0 (left, 1 (right), 2 (forward), 3 (backward), 4 (stop), 5 (land),
# 6 (ascend), 7 (descend), 8 (rotate_left), 9 (rotate_right)
tot_actions = 6
batch_size = 32 # size of the experience batch
tot_steps = 1000 # finite-horizont simulation
# 10x10^6 high number since training can be stopped at every time
tot_episodes = 50
steps_per_episodes = 20 # expressed in number step
noop_time = 2.0 # pause in seconds between actions
num_ground_plane = 21
actual_ground_index = 0
# episode_per_ground specify the number of episodes with the same ground
# plane
ground_counter = 1
episode_per_ground = 10
timer_total_start = time.time()
rospy.init_node("DeepReinforcedLanding")
rospy.loginfo("----- Deep Reinforced Landing Node -----")
rospy.Subscriber(
"/quadrotor/ardrone/bottom/ardrone/bottom/image_raw",
ROSImage,
image_callback,
queue_size=30) # Store the last 30 messages before discarding them
r = rospy.Rate(10) # 10hz
# Init session and networks
sess = tf.Session()
if (summary_folder == ""):
tf_summary_writer = tf.summary.FileWriter(
'./log/' + str(datetime.datetime.now().time()), sess.graph)
else:
tf_summary_writer = tf.summary.FileWriter(summary_folder, sess.graph)
policy_network = QNetwork(sess,
tot_actions=tot_actions,
image_shape=(screen_width, screen_height,
images_stack_size),
batch_size=batch_size,
network_name="policy_net")
init = tf.global_variables_initializer()
sess.run(init)
# Load Neural Networks weights from memory if a valid checkpoint path is passed
# if(os.path.isfile(policy_weights_path) == True and
# os.path.isfile(target_weights_path) == True):
try:
print("Loading weights from memory...")
policy_network.load_weights(policy_weights_path)
except:
print("Error while loading the weights!")
return
# start here
for episode in range(start_episode, tot_episodes):
# Reset the ground in the environment every 50 episodes (or
# episode_per_ground)
ground_index = episode / episode_per_ground
if ground_index != actual_ground_index or (episode == start_episode):
clean_world(ground_list)
generate_new_world(ground_list)
actual_ground_index = ground_index
timer_start = time.time()
actual_time = rospy.get_rostime()
rospy_start_time = actual_time.secs + actual_time.nsecs / 1000000000.0
cumulated_reward = 0
print("")
print("Episode: " + str(episode))
# 1-Accumulate the first state
reset_pose()
print "Reset pose!"
send_action("stop")
rospy.sleep(1.0)
state = _last_image
# create a stack of X images
image_t = np.stack([state] * images_stack_size, axis=2)
frame_episode = 0
for step in range(tot_steps):
# 2- Get the action following epsilon-greedy or through policy network.
# With probability epsilon take random action otherwise it takes
# an action from the policy network.
# Take a random action
# Here image_t is always ready and it can be given as input to
# the network
action_distribution = policy_network.return_action_distribution(
input_data=np.reshape(image_t, (1, 84, 84, images_stack_size)),
softmax=False)
action = np.argmax(action_distribution)
action = convert_action_int_to_str(action)
# action = get_random_action()
send_action(action)
rospy.sleep(noop_time)
# 3- Get the state_t1 repeating the action obtained at step-2 and add everything
# in the replay buffer. Then set state_t = state_t1
# get_image()
image_t1 = _last_image
# If the action taken is to land and the UAV is inside the landing
# BB, done will be calculated accordingly
done_reward = get_done_reward()
reward = done_reward.reward
done = done_reward.done
image_t1 = np.expand_dims(image_t1, 2)
# stack the images
image_t1 = np.append(image_t[:, :, 1:], image_t1, axis=2)
image_t = image_t1
cumulated_reward += reward
send_action("stop")
frame_episode = frame_episode + 1
# get_relative_pose(ground_index, episode, step, reward)
# At every step check if more than 30 seconds from the start passed.
# In that case, set done = True and end the episode
timer_stop = time.time()
# Stop the episode if the number of frame is more than a threshold
if frame_episode >= steps_per_episodes:
done = True
# if timer_stop - timer_start >= 30.0: # maximum time allowed
# #cumulated_reward += -1
# done = True
# When the episode is done
if done:
timer_stop = time.time()
actual_time = rospy.get_rostime()
rospy_stop_time = actual_time.secs + actual_time.nsecs / 1000000000.0
rospy_time_elapsed = rospy_stop_time - rospy_start_time
print("Tot Frame counter: " + str(frame_counter))
print("Time episode: " + str(timer_stop - timer_start) +
" seconds")
print("Ros time episode: " + str(rospy_time_elapsed) +
" seconds")
if cumulated_reward >= 0:
rospy.logwarn("Positive reward obtained!")
print("Cumulated reward: " + str(cumulated_reward))
print("Episode finished after {} timesteps".format(step + 1))
sys.stdout.flush()
# time.sleep(5)
with open(
"./results_" +
str(datetime.date.today().strftime("%m%d%Y")) + ".csv",
"a") as myfile:
boolean_reward = 0
if (cumulated_reward > 0):
boolean_reward = 1
ground_list = "mixed"
string_to_add = "mixed" + "," + str(episode) + "," + str(
step) + "," + str(cumulated_reward) + "," + str(
boolean_reward) + '\n'
myfile.write(string_to_add)
break
break
def main():
"""
Main function for training the DQN network in learning how to accomplish autonomous landing.
"""
# ground_list = ["water1",
# "water2",
# "water3",
# "water4",
# "water5",
# "water6",
# "water7",
# "water8",
# "water9",
# "water10"]
# ground_list = ["water11", "water12", "water13",
# "water14", "water15", "water16",
# "water17", "water18", "water19"]
ground_list = [
"pavement11", "pavement12", "pavement5", "pavement7", "pavement14",
"pavement15", "pavement16"
]
# NOTE: the ckp path must be modified in the code below
ckp_list = ["120750"]
# ATTENTION: If you want to restore files from a previous simulation you
# must pass valid values for these variables:
# policy_weights_path =
# '/home/pulver/Desktop/marine_descending/checkpoint/episode_94500/policy/policy_checkpoint.ckp'
summary_folder = "" # if empty the summary is written in ./log/ + current time
start_episode = 0 # change to last episode number
frame_counter = 0 # change to last number of frames
screen_width = 84 # original is 160
screen_height = 84 # original is 210
images_stack_size = 4
# Use only the first 5 actions for this simulation
# action_list = ['left', 'right', 'forward', 'backward', 'stop', 'descend']
# 0 (left, 1 (right), 2 (forward), 3 (backward), 4 (stop), 5 (descend)
tot_actions = 6
batch_size = 32 # size of the experience batch
tot_steps = 1000 # finite-horizont simulation
# 10x10^6 high number since training can be stopped at every time
steps_per_episodes = 40 # expressed in number step
noop_time = 2.0 # pause in seconds between actions
num_ground_plane = len(ground_list)
actual_ground_index = 0
# episode_per_ground specify the number of episodes with the same ground
# plane
episode_per_ground = 10
tot_episodes = episode_per_ground * num_ground_plane
accuracy_ground = 0.0
timer_total_start = time.time()
rospy.init_node("DRLTestNode")
rospy.loginfo("----- DRL Test Node -----")
# Create a subscriber fot the greyscale image
# rospy.Subscriber("/drl/grey_camera", ROSImage, image_callback)#TODO
# restore default
rospy.Subscriber(
"/quadrotor/ardrone/bottom/ardrone/bottom/image_raw",
ROSImage,
image_callback,
queue_size=30) # Store the last 30 messages before discarding them
# Create a publisher for changing light position
# light_pub = rospy.Publisher('/gazebo/default/light/modify', Light)
images_stack_size = 4
# r = rospy.Rate(10) # 10hz
# Init session and networks
sess = tf.Session()
policy_network = QNetwork(sess,
tot_actions=tot_actions,
image_shape=(screen_width, screen_height,
images_stack_size),
batch_size=batch_size,
network_name="policy_net")
init = tf.global_variables_initializer()
sess.run(init)
for i in range(len(ckp_list)):
ckp = ckp_list[i]
policy_weights_path = '/home/pulver/Desktop/sim11/episode_' + \
str(ckp) + '/policy/policy_checkpoint.ckp'
if (summary_folder == ""):
tf_summary_writer = tf.summary.FileWriter(
'./' + str(ckp) + '/log/' +
str(datetime.datetime.now().time()), sess.graph)
else:
tf_summary_writer = tf.summary.FileWriter(summary_folder,
sess.graph)
if (policy_weights_path != ""):
print("Loading weights from memory: " + str(policy_weights_path))
policy_network.load_weights(policy_weights_path)
else:
print("The networks path are empty. Choose appropriate weights!")
sys.exit()
# start here
for episode in range(start_episode, tot_episodes):
# Reset the ground in the environment every episode_per_ground
ground_index = episode / episode_per_ground
print "Current ground index: " + str(ground_index)
if episode % (
episode_per_ground) == 0 and episode != start_episode:
with open(
"./" + str(ckp) + "/accuracy_ckp" + str(ckp) + ".csv",
"a") as myfile:
accuracy_ground = accuracy_ground / \
(episode_per_ground * 1.0)
print "Final accuracy value: " + str(accuracy_ground)
string_to_add = ground_list[ground_index - 1] + \
"," + str(accuracy_ground) + "\n"
myfile.write(string_to_add)
if ground_index != actual_ground_index or (episode
== start_episode):
accuracy_ground = 0.0
clean_world(ground_list)
generate_new_world(ground_index, ground_list)
# Reset the light every 10 episodes
# if (episode % 10 == 0):
# # Change the position of the three point lights
# for i in range(0, 3):
# model_to_change = "user_point_light_" + str(i)
# x = STDrandom.uniform(-5.0, 5.0)
# y = STDrandom.uniform(-5.0, 5.0)
# z = STDrandom.uniform(1.0, 10.0)
# # os.system("rosrun gazebo_ros spawn_model -file ~/.gazebo/models/" + model_to_change +
# # "/model.sdf -sdf -model " + model_to_change + "_plane -x " + x + " -y " + y + " -z " + z + "")
# light_msg = Light()
# light_msg.pose.x = x
# light_msg.pose.y = y
# light_msg.pose.z = z
# light_msg.name = model_to_change
# light_pub.publish(light_msg)
# rospy.loginfo("Lights have been resetted!")
actual_ground_index = ground_index
timer_start = time.time()
actual_time = rospy.get_rostime()
rospy_start_time = actual_time.secs + actual_time.nsecs / 1000000000.0
frame_episode = 0
cumulated_reward = 0
epsilon_used = 0
send_action("takeoff")
print("")
print("Episode: " + str(episode))
# 0-Reset USV's position
os.system(
"rosservice call /gazebo/set_model_state '{model_state: { model_name: usv_model, pose: { position: { x: 0.0, y: 0.0 ,z: 5.0 }, orientation: {x: 0, y: 0.0, z: 0, w: 0.0 } }, twist: { linear: {x: 0.0 , y: 0 ,z: 0 } , angular: { x: 0.0 , y: 0 , z: 0.0 } } , reference_frame: world } }'"
)
# 1-Accumulate the first state
reset_pose()
print "Reset pose!"
send_action("stop")
rospy.sleep(1.0)
# get_image()
state = _last_image
# create a stack of X images
image_t = np.stack([state] * images_stack_size, axis=2)
for step in range(tot_steps):
# 2- Get the action following epsilon-greedy or through policy network.
# Here image_t is always ready and it can be given as input to
# the network
action_distribution = policy_network.return_action_distribution(
input_data=np.reshape(image_t,
(1, 84, 84, images_stack_size)),
softmax=False)
# print ""
# print action_distribution
action = np.argmax(action_distribution)
action = convert_action_int_to_str(action)
# print action
send_action(action)
rospy.sleep(noop_time)
# 3- Get the state_t1 repeating the action obtained at step-2 and add everything
# in the replay buffer. Then set state_t = state_t1
# get_image()
image_t1 = _last_image
send_action("stop")
# If the action taken is to land and the UAV is inside the landing
# BB, done will be calculated accordingly
done_reward = get_done_reward()
reward = done_reward.reward
done = done_reward.done
image_t1 = np.expand_dims(image_t1, 2)
# stack the images
image_t1 = np.append(image_t[:, :, 1:], image_t1, axis=2)
frame_counter += 1 # To call every time a frame is obtained
frame_episode += 1
image_t = image_t1
get_relative_pose(ground_index, episode, step, reward,
ground_list, ckp)
cumulated_reward += reward
# At every step check if more than 30 seconds from the start passed.
# In that case, set done = True and end the episode
timer_stop = time.time()
# Stop the episode if the number of frame is more than a
# threshold
if frame_episode >= steps_per_episodes:
done = True
# When the episode is done
if done:
timer_stop = time.time()
actual_time = rospy.get_rostime()
rospy_stop_time = actual_time.secs + actual_time.nsecs / 1000000000.0
rospy_time_elapsed = rospy_stop_time - rospy_start_time
print("Tot Frame counter: " + str(frame_counter))
print("Time episode: " + str(timer_stop - timer_start) +
" seconds")
print("Ros time episode: " + str(rospy_time_elapsed) +
" seconds")
if cumulated_reward >= 0:
rospy.logwarn("Positive reward obtained!")
print("Cumulated reward: " + str(cumulated_reward))
print("Episode finished after {} timesteps".format(step +
1))
sys.stdout.flush()
with open(
"./" + str(ckp) + "/results_ckp" + str(ckp) + "_" +
str(datetime.date.today().strftime("%m%d%Y")) +
".csv", "a") as myfile:
boolean_reward = 0
if (cumulated_reward > 0):
boolean_reward = 1
accuracy_ground += 1.0
print "Current accuracy value: " + str(accuracy_ground)
string_to_add = ground_list[ground_index] + "," + str(
episode) + "," + str(step) + "," + str(
cumulated_reward) + "," + str(
boolean_reward) + '\n'
myfile.write(string_to_add)
if episode == tot_episodes - 1:
with open(
"./" + str(ckp) + "/accuracy_ckp" + str(ckp) +
".csv", "a") as myfile:
accuracy_ground = accuracy_ground / \
(episode_per_ground * 1.0)
print "Final accuracy value: " + str(
accuracy_ground)
string_to_add = ground_list[actual_ground_index] + \
"," + str(accuracy_ground) + "\n"
myfile.write(string_to_add)
break
class DQN:
def __init__(self,
config,
game,
directory,
callback=None,
summary_writer=None):
self.game = game
self.actions = game.get_available_actions()
self.feedback_size = game.get_feedback_size()
self.callback = callback
self.summary_writer = summary_writer
self.config = config
self.batch_size = config['batch_size']
self.n_episode = config['num_episode']
self.capacity = config['capacity']
self.epsilon_decay = config['epsilon_decay']
self.epsilon_min = config['epsilon_min']
self.num_frames = config['num_frames']
self.num_nullops = config['num_nullops']
self.time_between_two_copies = config['time_between_two_copies']
self.input_scale = config['input_scale']
self.update_interval = config['update_interval']
self.directory = directory
self._init_modules()
def _init_modules(self):
# Replay memory
self.replay_memory = ReplayMemory(history_len=self.num_frames,
capacity=self.capacity,
batch_size=self.batch_size,
input_scale=self.input_scale)
input_shape = self.feedback_size + (self.num_frames, )
# Q-network
self.q_network = QNetwork(input_shape=input_shape,
n_outputs=len(self.actions),
network_type=self.config['network_type'],
scope='q_network')
# Target network
self.target_network = QNetwork(
input_shape=input_shape,
n_outputs=len(self.actions),
network_type=self.config['network_type'],
scope='target_network')
# Optimizer
self.optimizer = Optimizer(config=self.config,
feedback_size=self.feedback_size,
q_network=self.q_network,
target_network=self.target_network,
replay_memory=self.replay_memory)
# Ops for updating target network
self.clone_op = self.target_network.get_clone_op(self.q_network)
# For tensorboard
self.t_score = tf.placeholder(dtype=tf.float32,
shape=[],
name='new_score')
tf.summary.scalar("score", self.t_score, collections=['dqn'])
self.summary_op = tf.summary.merge_all('dqn')
def set_summary_writer(self, summary_writer=None):
self.summary_writer = summary_writer
self.optimizer.set_summary_writer(summary_writer)
def choose_action(self, sess, state, epsilon_greedy):
if numpy.random.binomial(1, epsilon_greedy) == 1:
action = random.choice(self.actions)
else:
x = numpy.asarray(numpy.expand_dims(state, axis=0) /
self.input_scale,
dtype=numpy.float32)
action = self.q_network.get_q_action(sess, x)[0]
return action
def play(self, action):
r, new_state, termination = self.game.play_action(action)
return r, new_state, termination
def update_target_network(self, sess):
sess.run(self.clone_op)
def train(self, sess, saver=None):
num_of_trials = -1
for episode in range(self.n_episode):
self.game.reset()
frame = self.game.get_current_feedback()
for _ in range(self.num_nullops):
r, new_frame, termination = self.play(action=0)
self.replay_memory.add(frame, 0, r, termination)
frame = new_frame
for _ in range(self.config['T']):
num_of_trials += 1
epsilon_greedy = self.epsilon_min + \
max(self.epsilon_decay - num_of_trials, 0) / \
self.epsilon_decay * (1 - self.epsilon_min)
print("epi {}, frame {}k: reward {}, eps {}".format(
episode, int(num_of_trials / 1000),
self.game.get_total_reward(), epsilon_greedy))
if num_of_trials % self.update_interval == 0:
self.optimizer.train_one_step(sess, num_of_trials,
self.batch_size)
state = self.replay_memory.phi(frame)
action = self.choose_action(sess, state, epsilon_greedy)
r, new_frame, termination = self.play(action)
self.replay_memory.add(frame, action, r, termination)
frame = new_frame
if num_of_trials % self.time_between_two_copies == 0:
self.update_target_network(sess)
self.save(sess, saver)
if self.callback:
self.callback()
if termination:
score = self.game.get_total_reward()
summary_str = sess.run(self.summary_op,
feed_dict={self.t_score: score})
self.summary_writer.add_summary(summary_str, num_of_trials)
self.summary_writer.flush()
break
def evaluate(self, sess):
for episode in range(self.n_episode):
self.game.reset()
frame = self.game.get_current_feedback()
for _ in range(self.num_nullops):
r, new_frame, termination = self.play(action=0)
self.replay_memory.add(frame, 0, r, termination)
frame = new_frame
for _ in range(self.config['T']):
print("episode {}, total reward {}".format(
episode, self.game.get_total_reward()))
state = self.replay_memory.phi(frame)
action = self.choose_action(sess, state, self.epsilon_min)
r, new_frame, termination = self.play(action)
self.replay_memory.add(frame, action, r, termination)
frame = new_frame
if self.callback:
self.callback()
if termination:
break
def save(self, sess, saver, model_name='model.ckpt'):
if saver:
try:
checkpoint_path = os.path.join(self.directory, model_name)
saver.save(sess, checkpoint_path)
except:
pass
def load(self, sess, saver, model_name='model.ckpt'):
if saver:
try:
checkpoint_path = os.path.join(self.directory, model_name)
saver.restore(sess, checkpoint_path)
except:
pass
def main():
"""
Main function for training the DQN network in learning how to accomplish autonomous landing.
"""
# ATTENTION: If you want to restore files from a previous simulation you
# must pass valid values for these variables:
policy_weights_path = '/home/pulver/Desktop/simulation_8/checkpoint/episode_20500/policy/policy_checkpoint.ckp'
summary_folder = "" # if empty the summary is written in ./log/ + current time
start_episode = 0 # change to last episode number
frame_counter = 0 # change to last number of frames
epsilon = 0.1
screen_width = 84 # original is 160
screen_height = 84 # original is 210
images_stack_size = 4
# Use only the first 5 actions for this simulation
# action_list = ['left', 'right', 'forward', 'backward', 'stop', 'descend']
# 0 (left, 1 (right), 2 (forward), 3 (backward), 4 (stop), 5 (descend)
tot_actions = 6
batch_size = 64 # size of the experience batch
tot_steps = 1000 # finite-horizont simulation
# 10x10^6 high number since training can be stopped at every time
tot_episodes = 210
steps_per_episodes = 40 # expressed in number step
noop_time = 2.0 # pause in seconds between actions
num_ground_plane = 70
actual_ground_index = 0
# episode_per_ground specify the number of episodes with the same ground plane
ground_counter = 1
episode_per_ground = 10
timer_total_start = time.time()
rospy.init_node("DRLTestNode")
rospy.loginfo("----- DRL Test Node -----")
# Create a subscriber fot the greyscale image
# rospy.Subscriber("/drl/grey_camera", ROSImage, image_callback)#TODO
# restore default
rospy.Subscriber(
"/quadrotor/ardrone/bottom/ardrone/bottom/image_raw", ROSImage, image_callback,queue_size=30) # Store the last 30 messages before discarding them
images_stack_size = 4
r = rospy.Rate(10) # 10hz
ground_list = ["asphalt11", "asphalt12", "asphalt13",
"brick11", "brick12", "brick13",
"grass11", "grass12", "grass13",
"pavement11", "pavement12", "pavement13",
"sand11", "sand12", "sand13",
"snow11", "snow12", "snow13",
"soil11", "soil12", "soil13"]
# Init session and networks
sess = tf.Session()
if(summary_folder == ""):
tf_summary_writer = tf.summary.FileWriter(
'./log/' + str(datetime.datetime.now().time()), sess.graph)
else:
tf_summary_writer = tf.summary.FileWriter(
summary_folder, sess.graph)
policy_network = QNetwork(sess, tot_actions=tot_actions, image_shape=(
screen_width, screen_height, images_stack_size), batch_size=batch_size, network_name="policy_net")
init = tf.global_variables_initializer()
sess.run(init)
if(policy_weights_path != ""):
print("Loading weights from memory: " + str(policy_weights_path))
policy_network.load_weights(policy_weights_path)
else:
print("The networks path are empty. Choose appropriate weights!")
sys.exit()
# start here
for episode in range(start_episode, tot_episodes):
# Reset the ground in the environment every 50 episodes (or episode_per_ground)
ground_index = episode / episode_per_ground
if ground_index != actual_ground_index or (episode == 0):
clean_world(ground_list)
generate_new_world(ground_index, ground_list)
actual_ground_index = ground_index
timer_start = time.time()
actual_time = rospy.get_rostime()
rospy_start_time = actual_time.secs + actual_time.nsecs / 1000000000.0
frame_episode = 0
cumulated_reward = 0
epsilon_used = 0
send_action("takeoff")
print("")
print("Episode: " + str(episode))
# 1-Accumulate the first state
reset_pose()
print "Reset pose!"
send_action("stop")
rospy.sleep(1.0)
# get_image()
state = _last_image
# create a stack of X images
image_t = np.stack([state] * images_stack_size, axis=2)
for step in range(tot_steps):
# 2- Get the action following epsilon-greedy or through policy network.
# Here image_t is always ready and it can be given as input to
# the network
epsilon_used_bool = False
if(np.random.random_sample(1) < epsilon):
epsilon_used += 1
action = get_random_action()
epsilon_used_bool = True
# Take the action from the policy network
else:
# Here image_t is always ready and it can be given as input to
# the network
action_distribution = policy_network.return_action_distribution(
input_data=np.reshape(image_t, (1, 84, 84, images_stack_size)), softmax=False)
action = np.argmax(action_distribution)
action = convert_action_int_to_str(action)
#print action
send_action(action)
rospy.sleep(noop_time)
# 3- Get the state_t1 repeating the action obtained at step-2 and add everything
# in the replay buffer. Then set state_t = state_t1
#get_image()
image_t1 = _last_image
send_action("stop")
# If the action taken is to land and the UAV is inside the landing BB, done will be calculated accordingly
done_reward = get_done_reward()
reward = done_reward.reward
done = done_reward.done
if epsilon_used_bool:
print " Step(" + str(step) + "), Action: " + action + ", Altitude: " + str(done_reward.z) + ", Reward: " + str(reward)
else:
print "#Step(" + str(step) + "), Action: " + action + ", Altitude: " + str(done_reward.z) + ", Reward: " + str(reward)
# ['left', 'right', 'forward', 'backward', 'stop', 'land'
print(" left:" + str(action_distribution[0][0][0]) + "; right:" + str(action_distribution[0][0][1]) + "; forward:" + str(action_distribution[0][0][2]) + "; backward:" + str(action_distribution[0][0][3]) + "; stop:" + str(action_distribution[0][0][4]) + "; descend:" + str(action_distribution[0][0][5]))
image_t1 = np.expand_dims(image_t1, 2)
# stack the images
image_t1 = np.append(image_t[:, :, 1:], image_t1, axis=2)
frame_counter += 1 # To call every time a frame is obtained
frame_episode += 1
image_t = image_t1
get_relative_pose(ground_index, episode, step, reward)
cumulated_reward += reward
# At every step check if more than 30 seconds from the start passed.
# In that case, set done = True and end the episode
timer_stop = time.time()
# Stop the episode if the number of frame is more than a threshold
if frame_episode >= steps_per_episodes:
done = True
# When the episode is done
if done:
timer_stop = time.time()
actual_time = rospy.get_rostime()
rospy_stop_time = actual_time.secs + actual_time.nsecs / 1000000000.0
rospy_time_elapsed = rospy_stop_time - rospy_start_time
print("Tot Frame counter: " + str(frame_counter))
print("Time episode: " + str(timer_stop - timer_start) + " seconds")
print("Ros time episode: " +
str(rospy_time_elapsed) + " seconds")
if cumulated_reward >= 0:
rospy.logwarn("Positive reward obtained!")
print("Cumulated reward: " + str(cumulated_reward))
print("Episode finished after {} timesteps".format(step + 1))
print("Epsilon used: " + str(epsilon_used) + " out of " + str(step + 1) + "(" + str(float((epsilon_used * 100.0) / (step + 1.0))) + "%)")
sys.stdout.flush()
time.sleep(5)
break
class DeepQ_agent:
"""
Represents the DQN agent.
"""
def __init__(self, env, hidden_units = None, network_LR=0.01, batch_size=1024, update_every=5, gamma=0.95):
"""
Creates a DQN agent.
:param env: game environment.
:type env: Class Snake_Env().
:param hidden_units: number of neurons in each layer.
:type hidden_units: tupple with dimension (1, 3).
:param network_LR: learning rate of the action-value neural network.
:type network_LR: float.
:param batch_size: size of the minibatch taken from the replay buffer.
:type batch_size: int.
:param update_every: number of iterations for updating the target qnetwork.
:type update_every: int
:param gamma: discount factor.
:type gamma: float.
"""
self.env = env
self.BATCH_SIZE = batch_size
self.GAMMA = gamma
self.NETWORK_LR = network_LR
self.MEMORY_CAPACITY = int(1e5)
self.ACTION_SIZE = env.ACTION_SPACE
self.HIDDEN_UNITS = hidden_units
self.UPDATE_EVERY = update_every
self.qnetwork_local = QNetwork(input_shape = self.env.STATE_SPACE,
hidden_units = self.HIDDEN_UNITS,
output_size = self.ACTION_SIZE,
learning_rate = self.NETWORK_LR)
self.qnetwork_target = QNetwork(input_shape = self.env.STATE_SPACE,
hidden_units = self.HIDDEN_UNITS,
output_size = self.ACTION_SIZE,
learning_rate = self.NETWORK_LR)
self.memory = ReplayMemory(self.MEMORY_CAPACITY, self.BATCH_SIZE)
#Temp variable
self.t = 0
def learn(self):
"""
Learn from memorized experience.
"""
if self.memory.__len__() > self.BATCH_SIZE:
states, actions, rewards, next_states, dones = self.memory.sample(self.env.STATE_SPACE)
#Calculating action-values using local network
target = self.qnetwork_local.predict(states, self.BATCH_SIZE)
#Future action-values using target network
target_val = self.qnetwork_target.predict(next_states, self.BATCH_SIZE)
#Future action-values using local network
target_next = self.qnetwork_local.predict(next_states, self.BATCH_SIZE)
max_action_values = np.argmax(target_next, axis=1) #action selection
for i in range(self.BATCH_SIZE):
if dones[i]:
target[i][actions[i]] = rewards[i]
else:
target[i][actions[i]] = rewards[i] + self.GAMMA*target_val[i][max_action_values[i]] #action evaluation
self.qnetwork_local.train(states, target, batch_size = self.BATCH_SIZE)
if self.t == self.UPDATE_EVERY:
self.update_target_weights()
self.t = 0
else:
self.t += 1
def act(self, state, epsilon=0.0):
"""
Chooses an action using an epsilon-greedy policy.
:param state: current state.
:type state: NumPy array with dimension (1, 18).
:param epsilon: epsilon used in epsilon-greedy policy.
:type epsilon: float
:return action: action chosen by the agent.
:rtype: int
"""
state = state.reshape((1,)+state.shape)
action_values = self.qnetwork_local.predict(state) #returns a vector of size = self.ACTION_SIZE
if random() > epsilon:
action = np.argmax(action_values) #choose best action - Exploitation
else:
action = randint(0, self.ACTION_SIZE-1) #choose random action - Exploration
return action
def add_experience(self, state, action, reward, next_state, done):
"""
Add experience to agent's memory.
"""
self.memory.add(state, action, reward, next_state, done)
def update_target_weights(self):
"""
Updates values of the Target network.
"""
self.qnetwork_target.model.set_weights(self.qnetwork_local.model.get_weights())
def main():
"""
Main function for training the DQN network in learning how to accomplish autonomous landing.
"""
# ATTENTION: If you want to restore files from a previous simulation you
# must pass valid values for these variables:
policy_weights_path_1 = '/home/pulver/Desktop/episode_113250/policy/policy_checkpoint.ckp'
root_images = "/home/pulver/Desktop/network_testing/" + \
str(datetime.datetime.now().time()) + "/"
# NOTE: openCV doesn't write in a folder that does not exist
if not os.path.exists(root_images):
os.makedirs(root_images)
source = 3 # NOTE: 1 for real drone, 2 for gazebo, 3 for URL
screen_width = 84 # original is 160
screen_height = 84 # original is 210
images_stack_size = 4
# Use only the first 5 actions for this simulation
# action_list = ['left', 'right', 'forward', 'backward', 'stop', 'descend']
# 0 (left, 1 (right), 2 (forward), 3 (backward), 4 (stop), 5 (descend)
tot_actions = 6
batch_size = 32 # size of the experience batch
rospy.init_node("DRLTrainingNode")
rospy.loginfo("----- DRL Training Node -----")
# Create a subscriber fot the greyscale image
# 1) Real drone
if source == 1:
rospy.Subscriber("/drl/grey_camera", ROSImage, image_callback)
elif source == 2:
# 2) Gazebo
rospy.Subscriber("/quadrotor/ardrone/bottom/ardrone/bottom/image_raw",
ROSImage, image_callback, queue_size=30) # Store the last 30 messages
elif source == 3:
# 3) URL
# video_stream_url = 'http://10.188.34.59:8080/videofeed'
video_stream_url = "http://10.5.5.9:8080/live/amba.m3u8"
bytes = ''
else:
print "Insert a correct source value (1 for real drone, 2 for gazebo, 3 for URL)"
images_stack_size = 4
tot_steps = 3000000 # finite-horizont simulation
r = rospy.Rate(1) # 10hz
# Init session and networks
sess = tf.Session()
summary_folder = "" # if empty the summary is written in ./log/ + current time
if(summary_folder == ""):
tf_summary_writer = tf.summary.FileWriter(
'./log/' + str(datetime.datetime.now().time()), sess.graph)
else:
tf_summary_writer = tf.summary.FileWriter(
summary_folder, sess.graph)
policy_network_1 = QNetwork(sess, tot_actions=tot_actions, image_shape=(
screen_width, screen_height, images_stack_size), batch_size=batch_size,
network_name="policy_net")
# Instructions for updating: Use `tf.global_variables_initializer
init = tf.global_variables_initializer()
sess.run(init)
# Load Neural Networks weights from memory if a valid checkpoint path is
# passed
if(policy_weights_path_1 != ""):
print("Loading weights 1 from memory...")
policy_network_1.load_weights(policy_weights_path_1)
else:
print("The networks path are empty.")
# sys.exit()
if source == 3:
state = acquire_frame(video_stream_url)
time.sleep(1)
state = _last_image
# Save first image
image_path = root_images + "image_0.png"
cv2.imwrite(image_path, state)
# 1 - Create a stack of X images
image_t = np.stack([state] * images_stack_size, axis=2)
matplotlib.interactive(True)
# fig = plt.figure()
f, (ax3, ax2) = plt.subplots(2, 1)
f.tight_layout()
# gs = gridspec.GridSpec(2, 2, width_ratios=[1, 1])
# gs.update(left=0.05, right=0.95, wspace=0.05, hspace=0)
# fig.set_size_inches(8, 4)
f.patch.set_facecolor('gray')
for step in range(1, 100000000):
##########################################
## CAMERA ##
##########################################
pad_size = 1
pad_value = 0
# print "Shape image_t:" + str(np.shape(image_t))
image = np.lib.pad(image_t[:, :, 0], (pad_size, pad_size),
'constant', constant_values=(pad_value, pad_value))
for d in range(1, images_stack_size):
image_stack_padded = np.lib.pad(
image_t[:, :, d], (pad_size, pad_size), 'constant', constant_values=(pad_value, pad_value))
image = np.append(image, image_stack_padded, axis=1)
# print"shape: " + str(np.shape(image))
# Plot in the first row the camera images
# ax1 = plt.subplot(2, 1, 1)
# ax1 = plt.subplot(gs[0, :])
# but first clear the old one
# ax1.clear()
# ax1.axis("off")
# specify greyscale instead BGR
# ax1.imshow(image, cmap='gray')
#########################################
# 2 - Forward in input to NN
# action_distribution_1 = policy_network_1.return_action_distribution(
# input_data=np.reshape(image_t, (1, 84, 84, images_stack_size)),
# softmax=False)
action_distribution_1, conv1, conv2, conv3 = policy_network_1.return_everything(
input_data=np.reshape(image_t, (1, 84, 84, images_stack_size)), softmax=False)
action = np.argmax(action_distribution_1)
action = convert_action_int_to_str(action)
print "######################"
print "Action distribution: " + str(action_distribution_1)
print "Action_1 selected: " + action
# print "Shape action dis:" + str(np.shape(action_distribution_1))
# print "Shape conv1:" + str(np.shape(conv1))
# print "Shape conv2:" + str(np.shape(conv2))
# print "Shape conv3:" + str(np.shape(conv3))
##########################################
## FILTERS ##
##########################################
ax3.clear()
ax3.axis("off")
padding = np.ones((1, 21 * 16))
# 1 Conv layer ###########################
conv1 = np.reshape(conv1, (21, 21, 32))
image_cv1_1 = np.reshape(conv1[:, :, 0:16], (21, 21 * 16), order='F')
image_cv1_2 = np.reshape(conv1[:, :, 16:32], (21, 21 * 16), order='F')
# image_cv1_1 = np.reshape(conv1[:, :, 0:8], (21, 21 * 8), order='F')
# image_cv1_2 = np.reshape(conv1[:, :, 8:16], (21, 21 * 8), order='F')
# image_cv1_3 = np.reshape(conv1[:, :, 16:24], (21, 21 * 8), order='F')
# image_cv1_4 = np.reshape(conv1[:, :, 24:32], (21, 21 * 8), order='F')
image_cv1 = np.concatenate(
(padding, image_cv1_1, image_cv1_2), axis=0)
# Save filters
filter_path = root_images + "filters/step_" + \
str(step) + "/"
if not os.path.exists(filter_path):
os.makedirs(filter_path)
filter_path = filter_path + "conv_1.jpg"
I = image_cv1
I8 = (((I - I.min()) / (I.max() - I.min())) * 255.9).astype(np.uint8)
image_cv1_resized = cv2.resize(I8, (84 * 4, 21 * 2),
interpolation=cv2.INTER_AREA)
img = Image.fromarray(image_cv1_resized)
img.save(filter_path)
# 2 Conv layer ###########################
padding = np.zeros((1, 11 * 32))
conv2 = np.reshape(conv2, (11, 11, 64))
image_cv2_1 = np.reshape(conv2[:, :, 0:32], (11, 11 * 32), order='F')
image_cv2_2 = np.reshape(conv2[:, :, 32:64], (11, 11 * 32), order='F')
# image_cv2_3 = np.reshape(conv2[:, :, 16:24], (21, 21 * 8), order='F')
# image_cv2_4 = np.reshape(conv2[:, :, 24:32], (21, 21 * 8), order='F')
image_cv2 = np.concatenate(
(padding, image_cv2_1, image_cv2_2), axis=0)
# Save filters
filter_path = root_images + "filters/step_" + \
str(step) + "/"
if not os.path.exists(filter_path):
os.makedirs(filter_path)
filter_path = filter_path + "conv_2.jpg"
I = image_cv2
I8 = (((I - I.min()) / (I.max() - I.min())) * 255.9).astype(np.uint8)
image_cv2_resized = cv2.resize(I8, (84 * 4, 11 * 2),
interpolation=cv2.INTER_AREA)
img = Image.fromarray(image_cv2_resized)
img.save(filter_path)
# 3 Conv layer ###########################
padding = np.ones((1, 11 * 32))
conv3 = np.reshape(conv3, (11, 11, 64))
image_cv3_1 = np.reshape(conv3[:, :, 0:32], (11, 11 * 32), order='F')
image_cv3_2 = np.reshape(conv3[:, :, 32:64], (11, 11 * 32), order='F')
# image_cv2_3 = np.reshape(conv2[:, :, 16:24], (21, 21 * 8), order='F')
# image_cv2_4 = np.reshape(conv2[:, :, 24:32], (21, 21 * 8), order='F')
image_cv3 = np.concatenate(
(padding, image_cv3_1, image_cv3_2), axis=0)
# Save filters
filter_path = root_images + "filters/step_" + \
str(step) + "/"
if not os.path.exists(filter_path):
os.makedirs(filter_path)
filter_path = filter_path + "conv_3.jpg"
I = image_cv3
I8 = (((I - I.min()) / (I.max() - I.min())) * 255.9).astype(np.uint8)
image_cv3_resized = cv2.resize(I8, (84 * 4, 11 * 2),
interpolation=cv2.INTER_AREA)
img = Image.fromarray(image_cv3_resized)
img.save(filter_path)
# Assemble final image ####################
image_input = np.reshape(image_t, (84, 84 * 4), order='F')
image_input_resized = cv2.resize(image, (84 * 4, 84),
interpolation=cv2.INTER_AREA)
final_image = np.concatenate(
(image_input_resized, image_cv1_resized, image_cv2_resized, image_cv3_resized))
# Plot the image
# ax3 = plt.subplot(gs[0, :])
# but first clear the old one
# specify greyscale instead BGR
ax3 = plt.subplot(2, 1, 1)
ax3.imshow(final_image, cmap='gray')
##########################################
## HISTOGRAM ##
##########################################
# print np.shape(action_distribution_1)
# print len(action_distribution_1[0])
ind = np.arange(len(action_distribution_1[0]))
# print ind
width = 0.4
# fig, ax = plt.subplots()
# ax2 = plt.subplot(gs[1, :])
ax2.clear()
ax2 = plt.subplot(2, 1, 2)
ax2.set_aspect(3)
rects = ax2.bar(0.4 + ind, action_distribution_1[0],
width=width, color='r', alpha=0.4)
rects[np.argmax(action_distribution_1)].set_color('g')
ax2.set_xticks(0.4 + ind + width / 2)
ax2.set_xticklabels(("left", "right", "forward",
"backward", "stop", "descend"))
for i in ax2.xaxis.get_ticklabels():
i.set_color("white")
ax2.set_ylim([0, 1])
# plt.xticks(fontsize=16)
# plt.show()
##########################################
# 3 - Send command and wait for translation
raw_input("Press a button to acquire images...")
# 4 - Acquire second frame and add to the stack
if source == 3:
global _last_image
_last_image = acquire_frame(video_stream_url)
time.sleep(1)
image_t1 = _last_image
# Save every one image image
image_path = root_images + "image_" + str(step) + ".png"
cv2.imwrite(image_path, image_t1)
image_t1 = np.expand_dims(image_t1, 2)
image_t1 = np.append(image_t[:, :, 1:], image_t1, axis=2)
# 5 - Forward stack in input to the network
image_t = image_t1
def main():
"""
Main function for training the DQN network in learning how to accomplish autonomous landing.
"""
# ATTENTION: If you want to restore files from a previous simulation you
# must pass valid values for these variables:
policy_weights_path = '/home/pulver/Desktop/episode_32400/policy/policy_checkpoint.ckp'
summary_folder = "" # if empty the summary is written in ./log/ + current time
start_episode = 0 # change to last episode number
frame_counter = 0 # change to last number of frames
screen_width = 84 # original is 160
screen_height = 84 # original is 210
images_stack_size = 4
# Use only the first 5 actions for this simulation
# 0 (left, 1 (right), 2 (forward), 3 (backward), 4 (stop), 5 (land),
# 6 (ascend), 7 (descend), 8 (rotate_left), 9 (rotate_right)
tot_actions = 6
batch_size = 32 # size of the experience batch
tot_steps = 1000 # finite-horizont simulation
# 10x10^6 high number since training can be stopped at every time
tot_episodes = 1050
steps_per_episodes = 20 # expressed in number step
noop_time = 2.0 # pause in seconds between actions
num_ground_plane = 21
episodes_per_ground_plane = tot_episodes / num_ground_plane
actual_ground_index = 0
timer_total_start = time.time()
rospy.init_node("DeepReinforcedLanding")
rospy.loginfo("----- Deep Reinforced Landing Node -----")
rospy.Subscriber(
"/quadrotor/ardrone/bottom/ardrone/bottom/image_raw", ROSImage, image_callback, queue_size=30) # Store the last 30 messages before discarding them
r = rospy.Rate(10) # 10hz
# Init session and networks
sess = tf.Session()
if(summary_folder == ""):
tf_summary_writer = tf.summary.FileWriter(
'./log/' + str(datetime.datetime.now().time()), sess.graph)
else:
tf_summary_writer = tf.summary.FileWriter(
summary_folder, sess.graph)
policy_network = QNetwork(sess, tot_actions=tot_actions, image_shape=(
screen_width, screen_height, images_stack_size), batch_size=batch_size, network_name="policy_net")
init = tf.global_variables_initializer()
sess.run(init)
# Load Neural Networks weights from memory if a valid checkpoint path is passed
# if(os.path.isfile(policy_weights_path) == True and
# os.path.isfile(target_weights_path) == True):
try:
print("Loading weights from memory...")
policy_network.load_weights(policy_weights_path)
except:
print("Error while loading the weights!")
return
print "Episode, step, action, reward, action_distribution"
# start here
for episode in range(start_episode, tot_episodes):
# Reset the ground in the environment every 25k frames
# ground_index = episode / 50
# if ground_index != actual_ground_index or episode == 0:
# generate_new_world(ground_index)
# actual_ground_index = ground_index
timer_start = time.time()
actual_time = rospy.get_rostime()
rospy_start_time = actual_time.secs + actual_time.nsecs / 1000000000.0
cumulated_reward = 0
# 1-Accumulate the first state
#reset_pose()
send_action("stop")
rospy.sleep(1.0)
state = _last_image
# create a stack of X images
image_t = np.stack([state] * images_stack_size, axis=2)
frame_episode = 0
pad_size = 3
pad_value = 255
for step in range(tot_steps):
# 2- Get the action following epsilon-greedy or through policy network.
# With probability epsilon take random action otherwise it takes
# an action from the policy network.
# Take a random action
image_stack_padded = [np.lib.pad(image_t[:,:,0], (pad_size, pad_size), 'constant', constant_values=(pad_value, pad_value)),
np.lib.pad(image_t[:,:,1], (pad_size, pad_size), 'constant', constant_values=(pad_value, pad_value)),
np.lib.pad(image_t[:,:,2], (pad_size, pad_size), 'constant', constant_values=(pad_value, pad_value)),
np.lib.pad(image_t[:,:,3], (pad_size, pad_size), 'constant', constant_values=(pad_value, pad_value))]
image_hstack = np.hstack(image_stack_padded)
cv2.imwrite("/home/pulver/Desktop/print_network_action_distribution/img_ep"+str(episode)+"_step" + str(step) + ".png", image_hstack )
# Here image_t is always ready and it can be given as input to
# the network
action_distribution = policy_network.return_action_distribution( input_data=np.reshape(image_t, (1, 84, 84, images_stack_size)), softmax=False)
action = np.argmax(action_distribution)
action = convert_action_int_to_str(action)
#action = get_random_action()
send_action(action)
rospy.sleep(noop_time)
# 3- Get the state_t1 repeating the action obtained at step-2 and add everything
# in the replay buffer. Then set state_t = state_t1
# get_image()
image_t1 = _last_image
# If the action taken is to land and the UAV is inside the landing
# BB, done will be calculated accordingly
done_reward = get_done_reward()
reward = done_reward.reward
done = done_reward.done
image_t1 = np.expand_dims(image_t1, 2)
# stack the images
image_t1 = np.append(image_t[:, :, 1:], image_t1, axis=2)
image_t = image_t1
cumulated_reward += reward
send_action("stop")
frame_episode = frame_episode + 1
#get_relative_pose(ground_index, episode, step, reward)
# At every step check if more than 30 seconds from the start passed.
# In that case, set done = True and end the episode
timer_stop = time.time()
print str(episode) + "," + str(step) + "," + action + "," + str(reward) + "," +str(action_distribution)
# Stop the episode if the number of frame is more than a threshold
if frame_episode >= steps_per_episodes:
done = True
# if timer_stop - timer_start >= 30.0: # maximum time allowed
# #cumulated_reward += -1
# done = True
# When the episode is done
if done:
break