class DeepQLearner(CompleteLearner):
""" Q-lambda is a variation of Q-learning that uses an eligibility trace. """
def __init__(self, n_input,actions, alpha=0.5, gamma=0.99, qlambda=0.9,explorer=EpsilonGreedyExplorer(epsilon=0.20,decay=1)):
CompleteLearner.__init__(self,actions)
controller = ActionValueNetwork(dimState=n_input, numActions=len(actions))
learner = NFQ()
learner.explorer = explorer
self.learning_agent = LearningAgent(controller, learner)
def setAction(self): #determine self.chosenAction
self.chosenAction = self.actions[int(round(self.learning_agent.getAction()))]
def learn(self):
print('epsilon:' + str(self.learning_agent.learner.explorer.epsilon))
if(self.t > 0 and self.t % 1000 == 0):
self.learning_agent.learner.learn()
self.learning_agent.lastobs = None
self.learning_agent.lastaction = None
self.learning_agent.lastreward = None
self.learning_agent.history.clear()
def setObservation(self,agent,environment):
environment.setObservation(agent)
self.t = environment.t
self.learning_agent.integrateObservation(self.observation)
print("observation =" + str(self.observation))
def cycle(self,agent,environment):
self.setObservation(agent,environment)
self.setAction()
self.performAction(agent,environment)
self.setReward(environment)
self.learn()
def performAction(self, agent, environment):
print('chosenAction='+str(self.chosenAction.function.__name__))
self.chosenAction.perform([agent,environment])
def reset(self):
pass
def setReward(self,environment):
self.r = environment.currentTask.reward_fun(environment.agent, environment)
self.R +=self.r
self.learning_agent.giveReward(self.r)
