本文整理匯總了Python中pybrain.rl.environments.episodic.EpisodicTask類的典型用法代碼示例。如果您正苦於以下問題：Python EpisodicTask類的具體用法？Python EpisodicTask怎麽用？Python EpisodicTask使用的例子？那麽, 這裏精選的類代碼示例或許可以為您提供幫助。
在下文中一共展示了EpisodicTask類的15個代碼示例，這些例子默認根據受歡迎程度排序。您可以為喜歡或者感覺有用的代碼點讚，您的評價將有助於係統推薦出更棒的Python代碼示例。
示例1: reset
def reset(self):
self.switched = False
EpisodicTask.reset(self)
if self.opponent.color == EuphoriaGame.BLACK:
# first move by opponent
self.opponent.game = self.env
EpisodicTask.performAction(self, (EuphoriaGame.BLACK,self.opponent.getAction()))示例2: __init__
def __init__(self, env, episodeLength):
EpisodicTask.__init__(self, env)
#self.inDim = 1
#self.outDim = 1
self.counter = 0
self.history = []
self.total = []
self.episodeLength = episodeLength示例3: __init__
def __init__(self, environment):
'''
Constructor
'''
EpisodicTask.__init__(self, environment)
self.prev_time = 0
self.current_time = 0
self.reward = 0示例4: performAction
def performAction(self, action):
""" Perform action on the underlying environment, i.e specify new asset
allocation.
Args:
action (np.array): new allocation
"""
# Cache new asset allocation for computing rewards
self.newAllocation = action
# Perform action
EpisodicTask.performAction(self, action)示例5: __init__
def __init__(self, size, opponent = None, **args):
EpisodicTask.__init__(self, PenteGame((size, size)))
self.setArgs(**args)
if opponent == None:
opponent = RandomGomokuPlayer(self.env)
elif isclass(opponent):
# assume the agent can be initialized without arguments then.
opponent = opponent(self.env)
if not self.opponentStart:
opponent.color = PenteGame.WHITE
self.opponent = opponent
self.minmoves = 9
self.maxmoves = self.env.size[0] * self.env.size[1]
self.reset()示例6: __init__
def __init__(self, size, opponent = None, **args):
EpisodicTask.__init__(self, CaptureGame(size))
self.setArgs(**args)
if opponent == None:
opponent = RandomCapturePlayer(self.env)
elif isclass(opponent):
# assume the agent can be initialized without arguments then.
opponent = opponent(self.env)
else:
opponent.game = self.env
if not self.opponentStart:
opponent.color = CaptureGame.WHITE
self.opponent = opponent
self.maxmoves = self.env.size * self.env.size
self.minmoves = 3
self.reset()示例7: performAction
def performAction(self, action):
# agent.game = self.env
if self.opponentStart:
EpisodicTask.performAction(self, (EuphoriaGame.WHITE, action))
else:
EpisodicTask.performAction(self, (EuphoriaGame.BLACK, action))
if not self.isFinished():
self.opponent.game = self.env
if self.opponentStart:
EpisodicTask.performAction(self, (EuphoriaGame.BLACK,self.opponent.getAction()))
else:
EpisodicTask.performAction(self, (EuphoriaGame.WHITE,self.opponent.getAction()))示例8: reset
def reset(self):
self.current_time = self.prev_time = 0.0
if const.USE_PERIODS:
self.current_time = self.prev_time = random.uniform(0,const.PERIODS)
self.current_time = const.MID_DAY
#print "ST", self.current_time
self.start_time = self.current_time
self.counter = 0
#choose a random node that is not the destination
node = grid.node_number(const.DESTINATION)
while(node == grid.node_number(const.DESTINATION)):
node = random.randint(0, const.NODES - 1)
#See what happens
if const.SAME_START:
node = 0
# while(node == grid.node_number(const.DESTINATION)):
# node = random.randint(0, const.NODES - 1)
self.start_node = node
self.env.reset_grid(self.current_time, node)
EpisodicTask.reset(self)示例9: getObservation
def getObservation(self):
""" An augmented observation of the underlying environment state that
also includes the current portfolio weights, right before
realloacation.
Returns:
state (np.array): the augmented state (size (P+1) * (I+1))
"""
# Observe past asset returns from the environment
pastReturns = EpisodicTask.getObservation(self)
# Return augmented state
return np.concatenate((pastReturns, self.currentAllocation))示例10: __init__
def __init__(self,
environment,
deltaP,
deltaF,
deltaS,
discount,
backtest=False):
""" Standard constructor for the asset allocation task.
Args:
environment (Environment): market environment object
deltaP (double): proportional transaction costs rate
deltaF (double): fixed transaction cost rate
deltaS (double): short selling borrowing cost rate
discount (double): discount factor
backtest (bool): flag for training mode or test mode
"""
# Initialize episodic task
EpisodicTask.__init__(self, environment)
# Transaction costs
self.deltaP = deltaP
self.deltaF = deltaF
self.deltaS = deltaS
# Discount factor
self.discount = discount
# Backtesting
self.backtest = backtest
# Report stores allocations and portfolio log-returns for backtesting
self.report = pd.DataFrame(columns=list(self.env.data.columns) +
['ptfLogReturn'])
# Initialize allocation
self.initializeAllocation()示例11: f
def f(self, x):
""" If a module is given, wrap it into a ModuleDecidingAgent before evaluating it.
Also, if applicable, average the result over multiple games. """
if isinstance(x, Module):
agent = ModuleDecidingPlayer(x, self.env, greedySelection = True)
elif isinstance(x, EuphoriaRandomPlayer):
agent = x
else:
raise NotImplementedError('Missing implementation for '+x.__class__.__name__+' evaluation')
res = 0
agent.game = self.env
self.opponent.game = self.env
for dummy in range(self.averageOverGames):
agent.color = -self.opponent.color
res += EpisodicTask.f(self, agent)
return res / float(self.averageOverGames)示例12: performAction
def performAction(self, action):
EpisodicTask.performAction(self, action)
if not self.isFinished():
EpisodicTask.performAction(self, self.opponent.getAction())示例13: reset
def reset(self):
self.switched = False
EpisodicTask.reset(self)
if self.opponent.color == CaptureGame.BLACK:
# first move by opponent
EpisodicTask.performAction(self, self.opponent.getAction())示例14: reset
def reset(self):
#i suppose this is the proper way to do it?
EpisodicTask.reset(self)
self.env.reset()示例15: reset
def reset(self):
EpisodicTask.reset(self)
self.env.reset()
self._ended = False