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'])
class QAgent(Agent):
alpha = 0.5
gamma = 0.9
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 _calculate(self):
self.action = self.estimator.getBestAction(self.state)
def learn(self):
""" go through whole episode and make Q-value updates. """
for episode in self.history:
for state, action, reward, nextstate in episode:
state = int(state)
action = int(action)
qvalue = self.estimator.getValue(state, action)
if nextstate != None:
nextstate = int(nextstate)
maxnext = self.estimator.getValue(nextstate, self.estimator.getBestAction(nextstate))
else:
maxnext = 0.
self.estimator.updateValue(state, action, qvalue + self.alpha * (reward + self.gamma * maxnext - qvalue))
