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):
""" 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):
action = self.action_from_joint_angles(action)
# Carry out the action based on angular velocities
EpisodicTask.performAction(self, action)
return
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):
action = self.action_from_joint_angles(action)
# Carry out the action based on angular velocities
EpisodicTask.performAction(self, action)
return
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 __init__(self, env=None, maxsteps=1000, desiredValue=0, location="Portland, OR"):
"""
:key env: (optional) an instance of a CartPoleEnvironment (or a subclass thereof)
:key maxsteps: maximal number of steps (default: 1000)
"""
self.location = location
self.airport_code = weather.airport(location)
self.desiredValue = desiredValue
if env is 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)]
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((-np.pi, np.pi))
# self.sensor_limits = [None] * 4
# actor between -10 and 10 Newton
self.actor_limits = [(-50, 50)]
def __init__(self, env=None, maxsteps=1000, desiredValue=0, location='Portland, OR'):
"""
:key env: (optional) an instance of a CartPoleEnvironment (or a subclass thereof)
:key maxsteps: maximal number of steps (default: 1000)
"""
self.location = location
self.airport_code = weather.airport(location)
self.desiredValue = desiredValue
if env is 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)]
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((-np.pi, np.pi))
# self.sensor_limits = [None] * 4
# actor between -10 and 10 Newton
self.actor_limits = [(-50, 50)]
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 performAction(self, action):
#Filtered mapping towards performAction of the underlying environment
#The standard Johnnie task uses a PID controller to controll directly angles instead of forces
#This makes most tasks much simpler to learn
isJoints=self.env.getSensorByName('JointSensor') #The joint angles
isSpeeds=self.env.getSensorByName('JointVelocitySensor') #The joint angular velocitys
act=(action+1.0)/2.0*(self.env.cHighList-self.env.cLowList)+self.env.cLowList #norm output to action intervall
action=tanh((act-isJoints-isSpeeds)*16.0)*self.maxPower*self.env.tourqueList #simple PID
EpisodicTask.performAction(self, action)
def __init__(self,environment, maxSteps, goalTolerance): EpisodicTask.__init__(self,environment) self.env = environment self.count = 0 self.atGoal = False self.MAX_STEPS = maxSteps self.GOAL_TOLERANCE = goalTolerance self.oldDist = 0 self.reward = 0
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, environment=None, batchSize=1):
self.batchSize = batchSize
if environment is None:
self.env = Lander()
else:
self.env = environment
EpisodicTask.__init__(self, self.env)
self.sensor_limits = [(0.0, 200.0), (0.0, 35.0), (0.0, 4.0),
(-6.0, 6.0), (-0.4, 0.4),
(-0.15, 0.15), (0.0, 200.0)]
def performAction(self, action):
#Filtered mapping towards performAction of the underlying environment
#The standard Johnnie task uses a PID controller to controll directly angles instead of forces
#This makes most tasks much simpler to learn
isJoints = self.env.getSensorByName('JointSensor') #The joint angles
isSpeeds = self.env.getSensorByName(
'JointVelocitySensor') #The joint angular velocitys
act = (action + 1.0) / 2.0 * (
self.env.cHighList - self.env.cLowList
) + self.env.cLowList #norm output to action intervall
action = tanh((act - isJoints - isSpeeds) *
16.0) * self.maxPower * self.env.tourqueList #simple PID
EpisodicTask.performAction(self, action)
def performAction(self, action):
#Filtered mapping towards performAction of the underlying environment
#The standard CCRL task uses a PID controller to controll directly angles instead of forces
#This makes most tasks much simpler to learn
self.oldAction = action
#Grasping as reflex depending on the distance to target - comment in for more easy grasping
if abs(abs(self.dist[:3]).sum())<2.0: action[15]=1.0 #self.grepRew=action[15]*.01
else: action[15]=-1.0 #self.grepRew=action[15]*-.03
isJoints=array(self.env.getSensorByName('JointSensor')) #The joint angles
isSpeeds=array(self.env.getSensorByName('JointVelocitySensor')) #The joint angular velocitys
act=(action+1.0)/2.0*(self.env.cHighList-self.env.cLowList)+self.env.cLowList #norm output to action intervall
action=tanh((act-isJoints-0.9*isSpeeds*self.env.tourqueList)*16.0)*self.maxPower*self.env.tourqueList #simple PID
EpisodicTask.performAction(self, action)
def performAction(self, action):
#Filtered mapping towards performAction of the underlying environment
#The standard Tetra2 task uses a PID controller to control directly angles instead of forces
#This makes most tasks much simpler to learn
isJoints=self.env.getSensorByName('JointSensor') #The joint angles
#print "Pos:", [int(i*10) for i in isJoints]
isSpeeds=self.env.getSensorByName('JointVelocitySensor') #The joint angular velocitys
#print "Speeds:", [int(i*10) for i in isSpeeds]
#print "Action", action, "cHighList",self.env.cHighList , self.env.cLowList
#act=(action+1.0)/2.0*(self.env.cHighList-self.env.cLowList)+self.env.cLowList #norm output to action intervall
#action=tanh(act-isJoints-isSpeeds)*self.maxPower*self.env.tourqueList #simple PID
#print "Action", act[:5]
EpisodicTask.performAction(self, action *self.maxPower*self.env.tourqueList)
def __init__(self, env=None, maxsteps=1000): """ :key env: (optional) an instance of a ChemotaxisEnv (or a subclass thereof) :key maxsteps: maximal number of steps (default: 1000) """ if env == None: env = ChemotaxisEnv() self.env = env EpisodicTask.__init__(self, env) self.N = maxsteps self.t = 0 #self.actor_limits = [(0,1), (0,1)] # scale (-1,1) to motor neurons self.sensor_limits = [(0,1), (0,1)] # scale sensor neurons to (-1,1)
def performAction(self, action):
#Filtered mapping towards performAction of the underlying environment
#The standard Tetra2 task uses a PID controller to control directly angles instead of forces
#This makes most tasks much simpler to learn
isJoints = self.env.getSensorByName('JointSensor') #The joint angles
#print "Pos:", [int(i*10) for i in isJoints]
isSpeeds = self.env.getSensorByName(
'JointVelocitySensor') #The joint angular velocitys
#print "Speeds:", [int(i*10) for i in isSpeeds]
#print "Action", action, "cHighList",self.env.cHighList , self.env.cLowList
#act=(action+1.0)/2.0*(self.env.cHighList-self.env.cLowList)+self.env.cLowList #norm output to action intervall
#action=tanh(act-isJoints-isSpeeds)*self.maxPower*self.env.tourqueList #simple PID
#print "Action", act[:5]
EpisodicTask.performAction(
self, action * self.maxPower * self.env.tourqueList)
def __init__(self, env = None, maxsteps = 1000):
if env == None:
env = CarEnvironment()
EpisodicTask.__init__(self, env)
self.N = maxsteps
self.t = 0
# (vel, deg, dist_to_goal, dir_of_goal, direction_diff)
self.sensor_limits = [(-30.0, 100.0),
(0.0, 360.0),
(0.0, variables.grid_size*2),
(-180.0, 180.0),
(-180.0, 180.0)]
self.actor_limits = [(-1.0, +4.5), (-90.0, +90.0)]
self.rewardscale = 100.0 / env.distance_to_goal
self.total_reward = 0.0
def __init__(self, env=None, maxsteps=1000, desiredValue = 0, tolorance = 0.3):
"""
:key env: (optional) an instance of a CartPoleEnvironment (or a subclass thereof)
:key maxsteps: maximal number of steps (default: 1000)
"""
self.desiredValue = desiredValue
EpisodicTask.__init__(self, env)
self.N = maxsteps
self.t = 0
self.tolorance = tolorance
# self.sensor_limits = [None] * 4
# actor between -10 and 10 Newton
self.actor_limits = [(-15, 15)]
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, environment, sort_beliefs=True, do_decay_beliefs=True, uniform_initial_beliefs=True, max_steps=30
):
EpisodicTask.__init__(self, environment)
self.verbose = False
self.listActions = False
self.env = environment
self.uniform_initial_beliefs = uniform_initial_beliefs
self.max_steps = max_steps
self.rewardscale = 1.0 # /self.max_steps
self.state_ids = {"init": 0, "grasped": 1, "lifted": 2, "placed": 3}
self.initialize()
def __init__(self, env=None, maxsteps=1000):
"""
:key env: (optional) an instance of a CartPoleEnvironment (or a subclass thereof)
:key maxsteps: maximal number of steps (default: 1000)
"""
if env == None:
env = CartPoleEnvironment()
EpisodicTask.__init__(self, env)
self.N = maxsteps
self.t = 0
# no scaling of sensors
self.sensor_limits = [None] * 2
# scale actor
self.actor_limits = [(-50, 50)]
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):
"""
:key env: (optional) an instance of a CartPoleEnvironment (or a subclass thereof)
:key maxsteps: maximal number of steps (default: 1000)
"""
if env == None:
env = CartPoleEnvironment()
EpisodicTask.__init__(self, env)
self.N = maxsteps
self.t = 0
# no scaling of sensors
self.sensor_limits = [None] * 2
# scale actor
self.actor_limits = [(-50, 50)]
def __init__(self, env):
EpisodicTask.__init__(self, env)
self.pause = False
# Holds all rewards given in each episode
self.reward_history = []
# The current timestep counter
self.count = 0
# The number of timesteps in the episode
self.epiLen = 1500
# Counts the task resets for incremental learning
self.incLearn = 0
# Sensor limit values can be used to normalize the sensors from
# (low, high) to (-1.0, 1.0) given the low and high values. We want to
# maintain all units by keeping the results unnormalized for now.
# Keeping the values of these lists at None skips the normalization.
self.sensor_limits = None
self.actor_limits = None
# Create all current joint observation attributes
self.joint_angles = [] # [rad]
self.joint_velocities = [] # [rad/s]
# Create the attribute for current tooltip position (x, y, z) [m]
self.tooltip_position = [] # [m]
# Create all joint angle [rad] limit attributes
self.joint_max_angles = []
self.joint_min_angles = []
# Create all joint velocity [rad/s] and torque [N*m] limit attributes
self.joint_max_velocities = []
self.joint_max_torques = []
# Call the setter function for the joint limit attributes
self.set_joint_angle_limits()
self.set_joint_velocity_limits()
self.set_joint_torque_limits()
return
def __init__(self, env):
EpisodicTask.__init__(self, env)
self.pause = False
# Holds all rewards given in each episode
self.reward_history = []
# The current timestep counter
self.count = 0
# The number of timesteps in the episode
self.epiLen = 1500
# Counts the task resets for incremental learning
self.incLearn = 0
# Sensor limit values can be used to normalize the sensors from
# (low, high) to (-1.0, 1.0) given the low and high values. We want to
# maintain all units by keeping the results unnormalized for now.
# Keeping the values of these lists at None skips the normalization.
self.sensor_limits = None
self.actor_limits = None
# Create all current joint observation attributes
self.joint_angles = [] # [rad]
self.joint_velocities = [] # [rad/s]
# Create the attribute for current tooltip position (x, y, z) [m]
self.tooltip_position = [] # [m]
# Create all joint angle [rad] limit attributes
self.joint_max_angles = []
self.joint_min_angles = []
# Create all joint velocity [rad/s] and torque [N*m] limit attributes
self.joint_max_velocities = []
self.joint_max_torques = []
# Call the setter function for the joint limit attributes
self.set_joint_angle_limits()
self.set_joint_velocity_limits()
self.set_joint_torque_limits()
return
def __init__(self, environment,
sort_beliefs=True,
do_decay_beliefs=True,
uniform_initial_beliefs=True,
max_steps=30):
EpisodicTask.__init__(self, environment)
self.verbose = False
self.listActions = False
self.env = environment
self.uniform_initial_beliefs = uniform_initial_beliefs
self.max_steps = max_steps
self.rewardscale = 1.0 #/self.max_steps
self.state_ids = {'init': 0, 'grasped': 1, 'lifted': 2, 'placed': 3}
self.initialize()
def __init__(self, env=None, maxsteps=1000):
"""
:key env: (optional) an instance of a ShipSteeringEnvironment (or a subclass thereof)
:key maxsteps: maximal number of steps (default: 1000)
"""
if env == None:
env = ShipSteeringEnvironment(render=False)
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=None, maxsteps=1000):
"""
:key env: (optional) an instance of a ShipSteeringEnvironment (or a subclass thereof)
:key maxsteps: maximal number of steps (default: 1000)
"""
if env == None:
env = ShipSteeringEnvironment(render=False)
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 = None, maxtime = 25, timestep = 0.1, logging = False):
"""
:key env: (optional) an instance of a DockingEnvironment (or a subclass thereof)
:key maxtime: (optional) maximum number per task (default: 25)
"""
if env == None:
env = DockingEnvironment()
EpisodicTask.__init__(self, env)
self.maxTime = maxtime
self.dt = timestep
self.env.dt = self.dt
self.t = 0
self.logging = logging
self.logfileName = 'logging_' + str() + '.txt'
# actions: u_l, u_r
self.actor_limits = [(-0.1, +0.1), (-0.1, +0.1), (0.0, 1.0)]
self.lastFitness = 0.0
self.bestFitness = 0.0
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))
self.actor_limits = None
def performAction(self, action):
#Filtered mapping towards performAction of the underlying environment
#The standard CCRL task uses a PID controller to controll directly angles instead of forces
#This makes most tasks much simpler to learn
self.oldAction = action
#Grasping as reflex depending on the distance to target - comment in for more easy grasping
if abs(abs(self.dist[:3]).sum()) < 2.0:
action[15] = 1.0 #self.grepRew=action[15]*.01
else:
action[15] = -1.0 #self.grepRew=action[15]*-.03
isJoints = array(
self.env.getSensorByName('JointSensor')) #The joint angles
isSpeeds = array(self.env.getSensorByName(
'JointVelocitySensor')) #The joint angular velocitys
act = (action + 1.0) / 2.0 * (
self.env.cHighList - self.env.cLowList
) + self.env.cLowList #norm output to action intervall
action = tanh(
(act - isJoints - 0.9 * isSpeeds * self.env.tourqueList) *
16.0) * self.maxPower * self.env.tourqueList #simple PID
EpisodicTask.performAction(self, action)
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 getObservation(self):
sensors = EpisodicTask.getObservation(self)
# Get the current joint angles [rad]
# The joint angle values will be between -pi and pi in radians
self.joint_angles = np.asarray(self.env.getSensorByName('JointSensor'))
# Get the current joint velocities [rad/s]
self.joint_velocities = np.asarray(self.env.getSensorByName('JointVelocitySensor'))
# Get the current tooltip position (x, y, z) [m]
self.tooltip_position = np.asarray(self.env.getSensorByName('tooltipPos'))
return sensors
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))
self.actor_limits = None
def __init__(self, environment,world):
self.world2d = world
""" All tasks are coupled to an environment. """
EpisodicTask.__init__(self,environment)
self.env = environment
self.reward_history = []
# limits for scaling of sensors and actors (None=disabled)
#self.actor_limits = [(-1.0,1.0),(-1.0,1.0),(-1.0,1.0),(-1.0,1.0)]
#self.actor_limits = [(-1.0,1.0),(-1.0,1.0),(-1.0,1.0)]
#self.actor_limits = [(-1.0,1.0),(-1.0,1.0),(-1.0,1.0),(-1.0,1.0),(-1.0,1.0)]
self.actor_limits = None
#self.actor_limits = [(0.0,1.0),(0.0,1.0)]#,(-0.3,0.3),(-0.3,0.3),(-1.0,1.0)]
#self.actor_limits = [(-2.0856,2.0856),(-0.6720+ 0.6720,0.5149 + 0.6720)]
#self.actor_limits = [(-1.64933,-0.00872)]
#self.actor_limits = [(-2.0856,2.0856),(-0.6720,0.5149)]
#self.sensor_limits = [(-1.64933,-0.00872),(-2.0856,2.0856),(-2.0856,2.0856),(0.00872,1.562),(0,640),(0,480),(0,300),(-2.0856,2.0856),(-0.6720,0.5149)] # joints, X,Y,radius, head
#self.sensor_limits = [(0.0,640.0),(0.0,480.0),(-2.0856,2.0856),(-0.6720,0.5149),(0.0,300.0)] # joints, X,Y,radius, head
#self.sensor_limits = [(-2.0856,2.0856),(-0.6720,0.5149)]#,(0.0,500.0),(-1.0,1.0),(-1.0,1.0)]
self.sensor_limits = [(0.0,160.0),(0.0,120.0),(0.0,80.0)]#,(-2.0856,2.0856),(-0.6720,0.5149)]
#self.sensor_limits = [(0.0,640.0),(0.0,480.0),(0.0,300.0)] # joints, X,Y,radius, head
#self.sensor_limits = [(-1.64933,-0.00872),(-2.0856,2.0856),(-2.0856,2.0856)]
#self.sensor_limits = [(0,500),(0,400)]
self.clipping = True
self.count = 0 #timestep counter
self.oldAction = zeros(environment.indim, float)
# needed for distance difference at first run
self.getObservation()
def getObservation(self):
sensors = EpisodicTask.getObservation(self)
# Get the current joint angles [rad]
# The joint angle values will be between -pi and pi in radians
self.joint_angles = np.asarray(self.env.getSensorByName('JointSensor'))
# Get the current joint velocities [rad/s]
self.joint_velocities = np.asarray(
self.env.getSensorByName('JointVelocitySensor'))
# Get the current tooltip position (x, y, z) [m]
self.tooltip_position = np.asarray(
self.env.getSensorByName('tooltipPos'))
return sensors
def performAction(self, action):
EpisodicTask.performAction(self, action)
self.t += self.dt
self.appendLog()
def reset(self):
self.steps = 0
EpisodicTask.reset(self)
def performAction(self, action):
self.t += 1
EpisodicTask.performAction(self, action)
def reset(self):
EpisodicTask.reset(self)
self.day = weather.daily(date="random")
self.t = 0
def performAction(self, action):
EpisodicTask.performAction(self, action*self.maxPower)
def reset(self):
EpisodicTask.reset(self)
self.t = 0
def __init__(self, env, animat):
EpisodicTask.__init__(self, env)
self.animat = animat
def performAction(self, action):
self.t += 1
EpisodicTask.performAction(self, action)
def reset(self):
self.steps = 0
EpisodicTask.reset(self)
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 reset(self):
EpisodicTask.reset(self)
self.t = 0
self.lastFitness = 0.0
self.bestFitness = 0.0
self.appendLog() # write first line!
def __init__(self):
self.environment = SimpleraceEnvironment()
EpisodicTask.__init__(self, self.environment)
self.t = 0
def reset(self):
EpisodicTask.reset(self)
self.total_reward = 0.0
