""" 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)
mkt = SmartMarket(case) agents = [] tasks = [] for g in bus1.generators: """ 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)