UPDATE_RATE = tc.UPDATE_RATE # Homotopy rate to update the networks
REPLAY_SIZE = tc.REPLAY_SIZE # Size of replay buffer
BATCH_SIZE = tc.BATCH_SIZE # Number of points to be fed in stochastic gradient
NH1 = tc.NH1
NH2 = tc.NH2 # Hidden layer size
range_esp = tc.range_esp
time_step = tc.time_step
reward_weights = [1., 0.0, 0.00]
sim_number = 2
RANDSET = 0
env = Robot("single_pendulum.urdf")
env_rend = Robot("single_pendulum.urdf", sim_number=sim_number) #for rendering
env.RANDSET = 0
step_expl = 0.
epi_expl = 0.
NX = 3 # ... training converges with q,qdot with 2x more neurones.
NU = 1 # Control is dim-1: joint torque
def angle_normalize(x):
return min(x % (2 * np.pi), abs(x % (2 * np.pi) - 2 * np.pi))
class QValueNetwork:
def __init__(self):
nvars = len(tf.compat.v1.trainable_variables())
# tip of Acrobot is considered to be in the center of gravity of link2
# -> goal position
length = .035 + .01 + 0.1088
goal = np.array([0,0,length])
env = Robot("double_pendulum.urdf")
env_rend = Robot("double_pendulum.urdf",sim_number=SIM_NUMBER) #for rendering
env.GUI_ENABLED=0
env.RANDSET = 0
env.SINCOS = 1
env.time_step= time_step
env.actuated_index =[2]
env.max_torque=5.0
env_rend.SINCOS = 1
env_rend.GUI_ENABLED = 1
env_rend.actuated_index = [2]
step_expl = 0.
POLICY_LEARNING_RATE = tc.POLICY_LEARNING_RATE # Base learning rate for the policy network
DECAY_RATE = tc.DECAY_RATE # Discount factor
UPDATE_RATE = tc.UPDATE_RATE # Homotopy rate to update the networks
REPLAY_SIZE = tc.REPLAY_SIZE # Size of replay buffer
BATCH_SIZE = tc.BATCH_SIZE # Number of points to be fed in stochastic gradient
NH1 = NH2 = tc.NH1 # Hidden layer size
range_esp = tc.range_esp
time_step = tc.time_step
SIM_NUMBER = 1.2
model = DDPG.load("ddpg_pendulum_stb_baselines_" + str(SIM_NUMBER))
robot = Robot("single_pendulum.urdf")
robot.sim_number = 1
robot.RANDSET = 0
robot.GUI_ENABLED = 1
robot.SINCOS = 1
path_log = "/home/pasquale/Desktop/thesis/thesis-code/1D_pendulum/stable_baselines/"
robot.time_step = time_step
robot.setupSim()
#Evaluate policy
#env.robot.stopSim()
#env = PendulumPyB()
#Check convergence
#confronta
#convergenza
#tempo di training
#average reward
rsum += reward
#print(rsum)
print(x)
if verbose: print('Lasted ',i,' timestep -- total reward:',rsum)
signal.signal(signal.SIGTSTP, lambda x,y:rendertrial()) # Roll-out when CTRL-Z is pressed
h_rwd = []
h_qva = []
h_ste = []
## Envirnonment Configuration ##
env.GUI_ENABLED=0
env.RANDSET = RANDSET
env.SINCOS = 1
env.time_step= time_step
env.setupSim()
## Training Loop ##
start_time = time.time()
for episode in range(1,NEPISODES):
env.resetRobot()
x = np.array([[env.states_sincos[1][0],env.states_sincos[1][1],
env.states_dot[1][3]]]) #remove .T
rsum = 0.0