print "Seed = %d" % RANDOM_SEED
np .random.seed (RANDOM_SEED)
random.seed (RANDOM_SEED)
#env = Pendulum(2,withDisplay=True) # Continuous pendulum
env = Pendulum(2,length=.5,mass=3.0,armature=.2,withDisplay=False)
env.withSinCos = False # State is dim-3: (cosq,sinq,qdot) ...
NX = env.nobs # ... training converges with q,qdot with 2x more neurones.
NU = env.nu # Control is dim-1: joint torque
env.vmax = 100.
env.Kf = np.diagflat([ 0.2, 2. ])
env.modulo = False
env.DT = 0.15
env.NDT = 1
#env.umax = 15.
#env.umax = (15.,15.)
env.umax = np.matrix([5.,10.]).T
NSTEPS = 32
env.qlow[1] = -np.pi
env.qup [1] = np.pi
# Shortcut function to convert SE3 to 7-dof vector.
M2gv = lambda M: XYZQUATToViewerConfiguration(se3ToXYZQUAT(M))
def place(objectId,M):
robot.viewer.gui.applyConfiguration(objectId, M2gv(M))
robot.viewer.gui.refresh() # Refresh the window.
DECAY_RATE = 0.99 # Discount factor
UPDATE_RATE = 0.01 # Homotopy rate to update the networks
REPLAY_SIZE = 10000 # Size of replay buffer
BATCH_SIZE = 64 # Number of points to be fed in stochastic gradient
NH1 = NH2 = 250 # Hidden layer size
RESTORE = "netvalues/actorcritic" # Previously optimize net weight
# (set empty string if no)
### --- Environment
env = Pendulum(1) # Continuous pendulum
env.withSinCos = True # State is dim-3: (cosq,sinq,qdot) ...
NX = env.nobs # ... training converges with q,qdot with 2x more neurones.
NU = env.nu # Control is dim-1: joint torque
env.vmax = 100.
env.DT = .15
env.NDT = 2
env.Kf = 0.2
NSTEPS = 30
### --- Q-value and policy networks
class QValueNetwork:
def __init__(self):
nvars = len(tf.trainable_variables())
x = tflearn.input_data(shape=[None, NX])
u = tflearn.input_data(shape=[None, NU])
netx1 = tflearn.fully_connected(x,
NH1,
BATCH_SIZE = 64 # Number of points to be fed in stochastic gradient
NH1 = NH2 = 250 # Hidden layer size
RESTORE = ""#"netvalues/actorcritic.15.kf2" # Previously optimize net weight
# (set empty string if no)
RENDERRATE = 20 # Render rate (rollout and plot) during training (0 = no)
#RENDERACTION = [ 'saveweights', 'draw', 'rollout' ]
REGULAR = True # Render on a regular grid vs random grid
### --- Environment
env = Pendulum(1) # Continuous pendulum
env.withSinCos = True # State is dim-3: (cosq,sinq,qdot) ...
NX = env.nobs # ... training converges with q,qdot with 2x more neurones.
NU = env.nu # Control is dim-1: joint torque
env.DT = .15
env.NDT = 2
env.Kf = 0.2
env.vmax = 100
RENDERACTION = [ 'draw', ]
'''
env = Pendulum(2,length=.5,mass=3.0,armature=10.)
env.withSinCos = True # State is dim-3: (cosq,sinq,qdot) ...
NX = env.nobs # ... training converges with q,qdot with 2x more neurones.
NU = env.nu # Control is dim-1: joint torque
env.DT = 0.2
env.NDT = 1
env.Kf = 10.0 # 1.0
BATCH_SIZE = 64 # Number of points to be fed in stochastic gradient
NH1 = NH2 = 250 # Hidden layer size
RESTORE = "netvalues/actorcritic.dt015.kf02.ep1300" # Previously optimize net weight
# (set empty string if no)
### --- Environment
env = Pendulum(1) # Continuous pendulum
env.withSinCos = True # State is dim-3: (cosq,sinq,qdot) ...
NX = env.nobs # ... training converges with q,qdot with 2x more neurones.
NU = env.nu # Control is dim-1: joint torque
env.vmax = 100.
env.Kf = 0.2
env.modulo = False
env.DT = 0.15
env.NDT = 1
NSTEPS = 32 # Number of intergration steps in horizon
NNODES = 8 # Number of shooting nodes
FNODES = NSTEPS / NNODES # Number of integration nodes per shooting interval ...
assert (not NSTEPS % NNODES) # ... should be an integer
### --- Q-value and policy networks
class QValueNetwork:
def __init__(self):
nvars = len(tf.trainable_variables())
x = tflearn.input_data(shape=[None, NX])
u = tflearn.input_data(shape=[None, NU])
