def performAction(self, action):
""" POMDP tasks, as they have discrete actions, can me used by providing either an index,
or an array with a 1-in-n coding (which can be stochastic). """
if type(action) == ndarray:
action = drawIndex(action, tolerant = True)
self.steps += 1
EpisodicTask.performAction(self, action)
def performAction(self, action):
""" POMDP tasks, as they have discrete actions, can me used by providing either an index,
or an array with a 1-in-n coding (which can be stochastic). """
if type(action) == ndarray:
action = drawIndex(action, tolerant=True)
self.steps += 1
EpisodicTask.performAction(self, action)
def reset(self):
self.reward[0] = 0.0
self.rawReward = 0.0
self.env.reset()
self.action = [self.env.dists[0]] * self.outDim
self.epiStep = 0
EpisodicTask.reset(self)
def __init__(self, environment):
EpisodicTask.__init__(self, environment)
self.reward_history = []
self.count = 0
# normalize to (-1, 1)
self.sensor_limits = [(-pi, pi), (-20, 20)]
self.actor_limits = [(-1, 1)]
def reset(self):
self.reward[0]=0.0
self.rawReward=0.0
self.env.reset()
self.action=[self.env.dists[0]]*self.outDim
self.epiStep=0
EpisodicTask.reset(self)
def __init__(self, environment):
EpisodicTask.__init__(self, environment)
self.reward_history = []
self.count = 0
# normalize to (-1, 1)
self.sensor_limits = [(-pi, pi), (-20, 20)]
self.actor_limits = [(-1, 1)]
def performAction(self, action):
""" a filtered mapping towards performAction of the underlying environment. """
# scaling
self.incStep()
action=(action+1.0)/2.0*self.dif+self.env.fraktMin*self.env.dists[0]
#Clipping the maximal change in actions (max force clipping)
action=clip(action, self.action-self.maxSpeed, self.action+self.maxSpeed)
EpisodicTask.performAction(self, action)
self.action=action.copy()
def performAction(self, action):
""" a filtered mapping towards performAction of the underlying environment. """
# scaling
self.incStep()
action = (action +
1.0) / 2.0 * self.dif + self.env.fraktMin * self.env.dists[0]
#Clipping the maximal change in actions (max force clipping)
action = clip(action, self.action - self.maxSpeed,
self.action + self.maxSpeed)
EpisodicTask.performAction(self, action)
self.action = action.copy()
def __init__(self, env):
EpisodicTask.__init__(self, env)
self.step=0
self.epiStep=0
self.reward=[0.0]
self.rawReward=0.0
self.obsSensors=["EdgesReal"]
self.rewardSensor=[""]
self.oldReward=0.0
self.plotString=["World Interactions", "Reward", "Reward on NoReward Task"]
self.inDim=len(self.getObservation())
self.outDim=self.env.actLen
self.dif=(self.env.fraktMax-self.env.fraktMin)*self.env.dists[0]
self.maxSpeed=self.dif/30.0
self.picCount=0
self.epiLen=1
def __init__(self, env):
EpisodicTask.__init__(self, env)
self.step = 0
self.epiStep = 0
self.reward = [0.0]
self.rawReward = 0.0
self.obsSensors = ["EdgesReal"]
self.rewardSensor = [""]
self.oldReward = 0.0
self.plotString = [
"World Interactions", "Reward", "Reward on NoReward Task"
]
self.inDim = len(self.getObservation())
self.outDim = self.env.actLen
self.dif = (self.env.fraktMax - self.env.fraktMin) * self.env.dists[0]
self.maxSpeed = self.dif / 30.0
self.picCount = 0
self.epiLen = 1
def __init__(self, env=None, maxsteps=1000):
"""
@param env: (optional) an instance of a CartPoleEnvironment (or a subclass thereof)
@param maxsteps: maximal number of steps (default: 1000)
"""
if env == None:
env = ShipSteeringEnvironment()
EpisodicTask.__init__(self, env)
self.N = maxsteps
self.t = 0
# scale sensors
# [h, hdot, v]
self.sensor_limits = [(-180.0, +180.0), (-180.0, +180.0), (-10.0, +40.0)]
# actions: thrust, rudder
self.actor_limits = [(-1.0, +2.0), (-90.0, +90.0)]
# scale reward over episode, such that max. return = 100
self.rewardscale = 100.0 / maxsteps / self.sensor_limits[2][1]
def __init__(self, env):
EpisodicTask.__init__(self, env)
self.maxPower=100.0 #Overall maximal tourque - is multiplied with relative max tourque for individual joint to get individual max tourque
self.reward_history = []
self.count = 0 #timestep counter
self.epiLen=500 #suggestet episodic length for normal Johnnie tasks
self.incLearn=0 #counts the task resets for incrementall learning
self.env.FricMu=20.0 #We need higher friction for Johnnie
self.env.dt=0.01 #We also need more timly resolution
# normalize standard sensors to (-1, 1)
self.sensor_limits=[]
#Angle sensors
for i in range(self.env.actLen):
self.sensor_limits.append((self.env.cLowList[i], self.env.cHighList[i]))
# Joint velocity sensors
for i in range(self.env.actLen):
self.sensor_limits.append((-20, 20))
#Norm all actor dimensions to (-1, 1)
self.actor_limits = [(-1, 1)]*env.actLen
def __init__(self, env=None, maxsteps=1000):
"""
@param env: (optional) an instance of a CartPoleEnvironment (or a subclass thereof)
@param maxsteps: maximal number of steps (default: 1000)
"""
if env == None:
env = ShipSteeringEnvironment()
EpisodicTask.__init__(self, env)
self.N = maxsteps
self.t = 0
# scale sensors
# [h, hdot, v]
self.sensor_limits = [(-180.0, +180.0), (-180.0, +180.0),
(-10.0, +40.0)]
# actions: thrust, rudder
self.actor_limits = [(-1.0, +2.0), (-90.0, +90.0)]
# scale reward over episode, such that max. return = 100
self.rewardscale = 100. / maxsteps / self.sensor_limits[2][1]
def __init__(self, env):
EpisodicTask.__init__(self, env)
self.maxPower = 100.0 #Overall maximal tourque - is multiplied with relative max tourque for individual joint to get individual max tourque
self.reward_history = []
self.count = 0 #timestep counter
self.epiLen = 500 #suggestet episodic length for normal Johnnie tasks
self.incLearn = 0 #counts the task resets for incrementall learning
self.env.FricMu = 20.0 #We need higher friction for Johnnie
self.env.dt = 0.01 #We also need more timly resolution
# normalize standard sensors to (-1, 1)
self.sensor_limits = []
#Angle sensors
for i in range(self.env.actLen):
self.sensor_limits.append(
(self.env.cLowList[i], self.env.cHighList[i]))
# Joint velocity sensors
for i in range(self.env.actLen):
self.sensor_limits.append((-20, 20))
#Norm all actor dimensions to (-1, 1)
self.actor_limits = [(-1, 1)] * env.actLen
def __init__(self, env):
EpisodicTask.__init__(self, env)
#Overall maximal tourque - is multiplied with relative max tourque for individual joint.
self.maxPower = 100.0
self.reward_history = []
self.count = 0 #timestep counter
self.epiLen = 1500 #suggestet episodic length for normal Johnnie tasks
self.incLearn = 0 #counts the task resets for incrementall learning
self.env.FricMu = 20.0 #We need higher friction for CCRL
self.env.dt = 0.002 #We also need more timly resolution
# normalize standard sensors to (-1, 1)
self.sensor_limits = []
#Angle sensors
for i in range(self.env.actLen):
self.sensor_limits.append(
(self.env.cLowList[i], self.env.cHighList[i]))
# Joint velocity sensors
for i in range(self.env.actLen):
self.sensor_limits.append((-20, 20))
#Norm all actor dimensions to (-1, 1)
self.actor_limits = [(-1, 1)] * env.actLen
self.oldAction = zeros(env.actLen, float)
self.dist = zeros(9, float)
self.dif = array([0.0, 0.0, 0.0])
self.target = array([-6.5, 1.75, -10.5])
self.grepRew = 0.0
self.tableFlag = 0.0
self.env.addSensor(SpecificBodyPositionSensor(['objectP00'],
"glasPos"))
self.env.addSensor(SpecificBodyPositionSensor(['palmLeft'], "palmPos"))
self.env.addSensor(
SpecificBodyPositionSensor(['fingerLeft1'], "finger1Pos"))
self.env.addSensor(
SpecificBodyPositionSensor(['fingerLeft2'], "finger2Pos"))
#we changed sensors so we need to update environments sensorLength variable
self.env.obsLen = len(self.env.getSensors())
#normalization for the task spezific sensors
for i in range(self.env.obsLen - 2 * self.env.actLen):
self.sensor_limits.append((-4, 4))
def __init__(self, env=None, maxsteps=1000):
"""
@param env: (optional) an instance of a CartPoleEnvironment (or a subclass thereof)
@param maxsteps: maximal number of steps (default: 1000)
"""
if env == None:
env = CartPoleEnvironment()
EpisodicTask.__init__(self, env)
self.N = maxsteps
self.t = 0
# scale position and angle, don't scale velocities (unknown maximum)
self.sensor_limits = [(-3, 3)] # , None, (-pi, pi), None]
for i in range(1, self.outdim):
if isinstance(self.env, NonMarkovPoleEnvironment) and i % 2 == 0:
self.sensor_limits.append(None)
else:
self.sensor_limits.append((-pi, pi))
self.sensor_limits = [None] * 4
# actor between -10 and 10 Newton
self.actor_limits = [(-10, 10)]
def __init__(self, env=None, maxsteps=1000):
"""
@param env: (optional) an instance of a CartPoleEnvironment (or a subclass thereof)
@param maxsteps: maximal number of steps (default: 1000)
"""
if env == None:
env = CartPoleEnvironment()
EpisodicTask.__init__(self, env)
self.N = maxsteps
self.t = 0
# scale position and angle, don't scale velocities (unknown maximum)
self.sensor_limits = [(-3, 3)] #, None, (-pi, pi), None]
for i in range(1, self.outdim):
if isinstance(self.env, NonMarkovPoleEnvironment) and i % 2 == 0:
self.sensor_limits.append(None)
else:
self.sensor_limits.append((-pi, pi))
self.sensor_limits = [None] * 4
# actor between -10 and 10 Newton
self.actor_limits = [(-10, 10)]
def __init__(self, env):
EpisodicTask.__init__(self, env)
#Overall maximal tourque - is multiplied with relative max tourque for individual joint.
self.maxPower=100.0
self.reward_history = []
self.count = 0 #timestep counter
self.epiLen=1500 #suggestet episodic length for normal Johnnie tasks
self.incLearn=0 #counts the task resets for incrementall learning
self.env.FricMu=20.0 #We need higher friction for CCRL
self.env.dt=0.002 #We also need more timly resolution
# normalize standard sensors to (-1, 1)
self.sensor_limits=[]
#Angle sensors
for i in range(self.env.actLen):
self.sensor_limits.append((self.env.cLowList[i], self.env.cHighList[i]))
# Joint velocity sensors
for i in range(self.env.actLen):
self.sensor_limits.append((-20, 20))
#Norm all actor dimensions to (-1, 1)
self.actor_limits = [(-1, 1)]*env.actLen
self.oldAction=zeros(env.actLen, float)
self.dist=zeros(9, float)
self.dif=array([0.0,0.0,0.0])
self.target=array([-6.5,1.75,-10.5])
self.grepRew=0.0
self.tableFlag=0.0
self.env.addSensor(SpecificBodyPositionSensor(['objectP00'], "glasPos"))
self.env.addSensor(SpecificBodyPositionSensor(['palmLeft'], "palmPos"))
self.env.addSensor(SpecificBodyPositionSensor(['fingerLeft1'], "finger1Pos"))
self.env.addSensor(SpecificBodyPositionSensor(['fingerLeft2'], "finger2Pos"))
#we changed sensors so we need to update environments sensorLength variable
self.env.obsLen=len(self.env.getSensors())
#normalization for the task spezific sensors
for i in range(self.env.obsLen-2*self.env.actLen):
self.sensor_limits.append((-4, 4))
def reset(self):
EpisodicTask.reset(self)
self.t = 0
def performAction(self, action):
self.t += 1
EpisodicTask.performAction(self, action)
def reset(self):
EpisodicTask.reset(self)
self.t = 0
def __init__(self, environment):
EpisodicTask.__init__(self, environment)
self.N = 15
self.t = 0
self.state = [0.0] * environment.dim
self.action = [0.0] * environment.dim
def performAction(self, action):
EpisodicTask.performAction(self, action)
self.action = action
def getObservation(self):
self.state = EpisodicTask.getObservation(self)
return self.state
def performAction(self, action):
EpisodicTask.performAction(self, action)
self.action = action
def __init__(self, environment):
EpisodicTask.__init__(self, environment)
self.N = 15
self.t = 0
self.state = [0.0] * environment.dim
self.action = [0.0] * environment.dim
def reset(self):
self.steps = 0
EpisodicTask.reset(self)
def reset(self):
self.steps = 0
EpisodicTask.reset(self)
def performAction(self, action):
self.t += 1
EpisodicTask.performAction(self, action)
def __init__(self):
self.environment = SimpleraceEnvironment()
EpisodicTask.__init__(self, self.environment)
self.t = 0
def getObservation(self):
self.state = EpisodicTask.getObservation(self)
return self.state
