def run_set_actions(waitTime=0, verbose=True):
rospy.init_node("test")
env = BaxterEnvironment()
rospy.on_shutdown(env.close)
cv2.namedWindow("Feed", cv2.WINDOW_NORMAL)
# Display starting environment
env._update_state()
if verbose:
print ""
print "horiz: " + str(env.horiz_dist)
print "vert: " + str(env.vert_dist)
cv2.imshow("Feed", env.image)
cv2.waitKey(waitTime)
# Create list of actions
actions = []
actions.append([0.9, 0.0])
actions.append([-0.9, 0.0])
actions.append([-0.9, 0.0])
actions.append([0.9, 0.0])
actions.append([0.0, 0.9])
actions.append([0.0, -0.9])
actions.append([0.0, -0.9])
actions.append([0.0, 0.9])
# Run actions untill interupted
i = -1
while not rospy.is_shutdown():
i = (i + 1) % len(actions)
action = actions[i]
s = time.time()
state, reward, done = env.step(actions[i])
e = time.time()
if verbose:
print ""
print "action: " + str(action)
print "horiz: " + str(state[0])
print "vert: " + str(state[1])
print "reward: " + str(reward)
print "done: " + str(done)
print "time: " + str(e - s)
# Display camera image
cv2.imshow("Feed", env.image)
cv2.waitKey(waitTime)
def run_random(waitTime=0, verbose=True):
rospy.init_node("test")
env = BaxterEnvironment()
env.reset()
rospy.on_shutdown(env.close)
cv2.namedWindow("Feed", cv2.WINDOW_NORMAL)
# Run randomly untill interupteds = time.time()
done = True
while not rospy.is_shutdown():
if done:
# Reset
state = env.reset()
if verbose:
print ""
print "horiz: " + str(state[0])
print "vert: " + str(state[1])
done = False
else:
# Random action
action = [random.uniform(-1, 1), random.uniform(-1, 1)]
s = time.time()
state, reward, done = env.step(action)
e = time.time()
if verbose:
print ""
print "action: " + str(action)
print "horiz: " + str(state[0])
print "vert: " + str(state[1])
print "reward: " + str(reward)
print "done: " + str(done)
print "time: " + str(e - s)
# Display camera image
cv2.imshow("Feed", env.image)
cv2.waitKey(waitTime)
def run():
rospy.init_node("test")
env = BaxterEnvironment((1280, 800))
retina = Retina(1280, 800)
env.reset()
rospy.on_shutdown(env.close)
cv2.namedWindow("Feed", cv2.WINDOW_NORMAL)
# Key action mapping
actions = {ord('a'): [0.1, 0], ord('s'): [-0.1, 0],
ord('d'): [0, 0.1], ord('f'): [0, -0.1],
ord('q'): [1, 0], ord('w'): [-1, 0],
ord('e'): [0, 1], ord('r'): [0, -1]}
# Run until interupted or 'esc' pressed
done = True
key = None
ep_step = 0
while not rospy.is_shutdown():
if done:
# Reset environment
state = env.reset()
print ""
print "horiz: " + str(state[0])
print "vert: " + str(state[1])
print "x pos: " + str(state[2])
print "y pos: " + str(state[3])
done = False
ep_step = 0
else:
if key in actions:
ep_step = ep_step + 1
# If valid key pressed execute corresponding action
action = actions[key]
state, reward, done = env.step(action)
print ""
print "action: " + str(action)
print "horiz: " + str(state[0])
print "vert: " + str(state[1])
print "x pos: " + str(state[2])
print "y pos: " + str(state[3])
print "reward: " + str(reward)
print "done: " + str(done)
done = done or (ep_step == 15)
# Display image until key pressed
cv2.imshow("Feed", retina.sample(env.image))
key = cv2.waitKey(0)
# If 'esc' pressed exit program
if key == 27:
break
state_dict = state_dict + "retina_"
state_dict = (state_dict + args.network + "/state_dicts/net_"
+ str(args.epoch))
resnet.load_state_dict(torch.load(state_dict))
# Replace final layer with flatten layer
layers = list(resnet.children())[:-1]
layers.append(Flatten())
resnet = nn.Sequential(*layers)
# Environment variables
STATE_DIM = features + 2
ACTION_DIM = 2
# Training variables
ENVIRONMENT = BaxterEnvironment((1280, 800), True)
INIT_EXPLORE = 2000
MAX_STEPS = 100000
MAX_EP_STEPS = 15
UPDATES_PER_STEP = 1
DATA = (os.path.dirname(os.path.realpath(__file__))
+ "/baxter_center/data/")
DATA = None
MODEL = (os.path.dirname(os.path.realpath(__file__))
+ "/baxter_center/mlp_")
RESULT = (os.path.dirname(os.path.realpath(__file__))
+ "/baxter_center/mlp_")
if args.use_retina:
MODEL = MODEL + "retina_"
RESULT = RESULT + "retina_"
MODEL = MODEL + str(features) + "vector/state_dicts/"
# Create folders to save data
DATA_FOLDER = (os.path.dirname(
os.path.realpath(__file__)) + "/baxter_center/image_data/")
NORM_IMAGES = DATA_FOLDER + "images/"
RETINA_IMAGES = DATA_FOLDER + "retina_images/"
if not os.path.isdir(NORM_IMAGES):
os.makedirs(NORM_IMAGES)
if not os.path.isdir(RETINA_IMAGES):
os.makedirs(RETINA_IMAGES)
# Create preprocessors
preprocessor = BaxterPreprocessor()
# Create environment
rospy.init_node('data_gathering_process')
env = BaxterEnvironment((1280, 800))
retina = Retina(1280, 800)
rospy.on_shutdown(env.close)
# Create function for exploration
noise_function = NormalActionNoise(actions=ACTION_DIM)
# Create tensors for storing data
states = torch.ones((SIZE, STATE_DIM), dtype=torch.float)
timestep_ep = 0
done = True
state = None
image = None
index = 0
while index < SIZE:
PREPROCESSOR = BaxterImagePreprocessor(resnet, args.visualise)
S_NORMALIZER = FeatureNormalizer(2)
R_NORMALIZER = None
MODEL_FOLDER = (os.path.dirname(os.path.realpath(__file__)) +
"/baxter_center/mlp_normRs/state_dicts/")
CHECKPOINT = 100000
# Load agent from checkpoint
agent = Ddpg(REPLAY_SIZE, BATCH_SIZE, NOISE_FUNCTION, INIT_NOISE,
FINAL_NOISE, EXPLORATION_LEN, REWARD_SCALE, ACTOR,
ACTOR_OPTIM, CRITIC, CRITIC_OPTIM, PREPROCESSOR, S_NORMALIZER,
R_NORMALIZER)
agent.load_checkpoint(MODEL_FOLDER, CHECKPOINT)
# Set seeds
torch.manual_seed(15)
torch.cuda.manual_seed(15)
np.random.seed(15)
random.seed(15)
# Create environment
if args.network is not None:
env = BaxterEnvironment((1280, 800), True)
else:
env = BaxterEnvironment()
rospy.on_shutdown(env.close)
# Evaluate and print performance
print "Performance: %0.3f +- %0.3f" % (agent.evaluate(
env, MAX_STEPS, EVAL_EP))
if __name__ == '__main__':
rospy.init_node("training process")
# Set seeds
torch.manual_seed(15)
torch.cuda.manual_seed(15)
np.random.seed(15)
random.seed(15)
# Environment variables
STATE_DIM = 4
ACTION_DIM = 2
# Training variables
ENVIRONMENT = BaxterEnvironment()
INIT_EXPLORE = 2000
MAX_STEPS = 300000
MAX_EP_STEPS = 15
UPDATES_PER_STEP = 1
DATA = (os.path.dirname(os.path.realpath(__file__)) +
"/baxter_center/data/")
DATA = None
MODEL = (os.path.dirname(os.path.realpath(__file__)) +
"/baxter_center/mlp_normRs/state_dicts/")
RESULT = (os.path.dirname(os.path.realpath(__file__)) +
"/baxter_center/mlp_normRs/results/")
EVAL_FREQ = 2000
EVAL_EP = 20
CHECKPOINT = None
STATE_DIM = 4
ACTION_DIM = 2
MAX_EP_STEPS = 100
# Create folder to save data
DATA_FOLDER = (os.path.dirname(os.path.realpath(__file__)) +
"/baxter_center/data/")
if not os.path.isdir(DATA_FOLDER):
os.makedirs(DATA_FOLDER)
# Create preprocessors
preprocessor = BaxterPreprocessor()
# Create environment
rospy.init_node('data_gathering_process')
env = BaxterEnvironment()
rospy.on_shutdown(env.close)
# Create function for exploration
noise_function = NOISE_FUNCTION = NormalActionNoise(actions=ACTION_DIM)
# Create tensors for storing data
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
states = torch.ones((SIZE, STATE_DIM), dtype=torch.float, device=device)
actions = torch.ones((SIZE, ACTION_DIM), dtype=torch.float, device=device)
next_states = torch.ones((SIZE, STATE_DIM),
dtype=torch.float,
device=device)
rewards = torch.ones((SIZE, 1), dtype=torch.float, device=device)
dones = torch.ones((SIZE, 1), dtype=torch.float, device=device)