def __init__(self, faClass=Linear, resetFA=True, ordered=False, vectorblock=False):
""" initialize the agent with the estimatorClass. """
Agent.__init__(self)
self.faClass = faClass
self.resetFA = resetFA
self.ordered = ordered
self.vectorblock = vectorblock
def _setup(self, conditions):
""" if agent is discrete in states and actions create Q-Table. """
Agent._setup(self, conditions)
if not (self.conditions['discreteStates'] and self.conditions['discreteActions']):
raise AgentException('QAgent expects discrete states and actions. Use adapter or a different environment.')
self.estimator = TableEstimator(self.conditions['stateNum'], self.conditions['actionNum'])
def _setup(self, conditions):
""" if agent is discrete in states and actions create Q-Table. """
Agent._setup(self, conditions)
if not (self.conditions['discreteStates'] == False and self.conditions['discreteActions']):
raise AgentException('FQIAgent expects continuous states and discrete actions. Use adapter or a different environment.')
if self.vectorblock:
self.estimator = VectorBlockEstimator(self.conditions['stateDim'], self.conditions['actionNum'], faClass=self.faClass, ordered=self.ordered)
else:
self.estimator = FAEstimator(self.conditions['stateDim'], self.conditions['actionNum'], faClass=self.faClass, ordered=self.ordered)
def _setup(self, conditions):
""" if agent is discrete in states and actions create Q-Table. """
Agent._setup(self, conditions)
if not (self.conditions['discreteStates'] == False and self.conditions['discreteActions'] == False):
raise AgentException('BASAgent expects continuous states and actions. Use adapter or a different environment.')
self.estimator = FAEstimator(self.conditions['stateDim'] + self.conditions['actionDim'], 2**self.conditions['actionDim'], self.faClass)
# change history to store bas-extended experiences
self.history = History(conditions['stateDim']+self.conditions['actionDim'] , 1)
def __init__(self, faClass=Linear):
""" initialize the agent with the estimatorClass. """
Agent.__init__(self)
self.amin = -1.
self.amax = 1.
self.nres = 3
# store (decision,action) tuples for one action in the list
self.decisions = []
self.faClass = faClass
def giveReward(self, reward):
""" additionally remember the chosen action to not draw it again. """
if self.ordered:
self.estimator.rememberAction(self.action)
Agent.giveReward(self, reward)
def newEpisode(self):
""" reset the memory. """
Agent.newEpisode(self)
if self.ordered:
self.estimator.resetMemory()
