本文整理汇总了Python中pybrain.rl.agents.LearningAgent.actaspg方法的典型用法代码示例。如果您正苦于以下问题：Python LearningAgent.actaspg方法的具体用法？Python LearningAgent.actaspg怎么用？Python LearningAgent.actaspg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.agents.LearningAgent的用法示例。
在下文中一共展示了LearningAgent.actaspg方法的1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: ParticipantEnvironment
# 需要导入模块: from pybrain.rl.agents import LearningAgent [as 别名]
# 或者: from pybrain.rl.agents.LearningAgent import actaspg [as 别名]
""" Create an environment for each agent with an asset and a market. """
env = ParticipantEnvironment(g, mkt, n_offbids=2)
""" Create a task for the agent to achieve. """
task = ProfitTask(env)
""" Build an artificial neural network for the agent. """
net = buildNetwork(task.outdim, task.indim, bias=False, outputbias=False)
# net._setParameters(array([9]))
""" Create a learning agent with a learning algorithm. """
agent = LearningAgent(module=net, learner=ENAC())
""" Initialize parameters (variance). """
# agent.setSigma([-1.5])
""" Set learning options. """
agent.learner.alpha = 2.0
# agent.learner.rprop = True
agent.actaspg = False
# agent.disableLearning()
agents.append(agent)
tasks.append(task)
""" The Experiment will coordintate the interaction of the given agents and
their associated tasks. """
experiment = MarketExperiment(tasks, agents, mkt)
experiment.setRenderer(ExperimentRenderer())
""" Instruct the experiment to coordinate a set number of interactions. """
experiment.doInteractions(3)