本文整理汇总了Python中pybrain.rl.environments.EpisodicTask.__init__方法的典型用法代码示例。如果您正苦于以下问题:Python EpisodicTask.__init__方法的具体用法?Python EpisodicTask.__init__怎么用?Python EpisodicTask.__init__使用的例子?那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类pybrain.rl.environments.EpisodicTask
的用法示例。
在下文中一共展示了EpisodicTask.__init__方法的15个代码示例,这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞,您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: __init__
# 需要导入模块: from pybrain.rl.environments import EpisodicTask [as 别名]
# 或者: from pybrain.rl.environments.EpisodicTask import __init__ [as 别名]
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)]
示例2: __init__
# 需要导入模块: from pybrain.rl.environments import EpisodicTask [as 别名]
# 或者: from pybrain.rl.environments.EpisodicTask import __init__ [as 别名]
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)]
示例3: __init__
# 需要导入模块: from pybrain.rl.environments import EpisodicTask [as 别名]
# 或者: from pybrain.rl.environments.EpisodicTask import __init__ [as 别名]
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
示例4: __init__
# 需要导入模块: from pybrain.rl.environments import EpisodicTask [as 别名]
# 或者: from pybrain.rl.environments.EpisodicTask import __init__ [as 别名]
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)]
示例5: __init__
# 需要导入模块: from pybrain.rl.environments import EpisodicTask [as 别名]
# 或者: from pybrain.rl.environments.EpisodicTask import __init__ [as 别名]
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
示例6: __init__
# 需要导入模块: from pybrain.rl.environments import EpisodicTask [as 别名]
# 或者: from pybrain.rl.environments.EpisodicTask import __init__ [as 别名]
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)
示例7: __init__
# 需要导入模块: from pybrain.rl.environments import EpisodicTask [as 别名]
# 或者: from pybrain.rl.environments.EpisodicTask import __init__ [as 别名]
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)]
示例8: __init__
# 需要导入模块: from pybrain.rl.environments import EpisodicTask [as 别名]
# 或者: from pybrain.rl.environments.EpisodicTask import __init__ [as 别名]
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
示例9: __init__
# 需要导入模块: from pybrain.rl.environments import EpisodicTask [as 别名]
# 或者: from pybrain.rl.environments.EpisodicTask import __init__ [as 别名]
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)]
示例10: __init__
# 需要导入模块: from pybrain.rl.environments import EpisodicTask [as 别名]
# 或者: from pybrain.rl.environments.EpisodicTask import __init__ [as 别名]
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()
示例11: __init__
# 需要导入模块: from pybrain.rl.environments import EpisodicTask [as 别名]
# 或者: from pybrain.rl.environments.EpisodicTask import __init__ [as 别名]
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
示例12: __init__
# 需要导入模块: from pybrain.rl.environments import EpisodicTask [as 别名]
# 或者: from pybrain.rl.environments.EpisodicTask import __init__ [as 别名]
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]
示例13: __init__
# 需要导入模块: from pybrain.rl.environments import EpisodicTask [as 别名]
# 或者: from pybrain.rl.environments.EpisodicTask import __init__ [as 别名]
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
示例14: __init__
# 需要导入模块: from pybrain.rl.environments import EpisodicTask [as 别名]
# 或者: from pybrain.rl.environments.EpisodicTask import __init__ [as 别名]
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
示例15: __init__
# 需要导入模块: from pybrain.rl.environments import EpisodicTask [as 别名]
# 或者: from pybrain.rl.environments.EpisodicTask import __init__ [as 别名]
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