def __init__(self, name):
super(Player,
self).__init__(NineProblem(),
temperature_function=make_exponential_temperature(
1000000, 0.01),
discount_factor=0.9)
self.name = name
def __init__(self, play_with):
super(NuevePlayer,
self).__init__(NueveProblem(),
temperature_function=make_exponential_temperature(
1000000, 0.01),
discount_factor=0.4)
self.name = play_with
def __init__(self, name):
super(PapelitosPlayer,
self).__init__(ProblemaPapelitos(),
temperature_function=make_exponential_temperature(
1000000, 0.01),
discount_factor=0.4)
self.name = name
def __init__(self, agentName, problem):
super(GenericPlayer,
self).__init__(problem,
temperature_function=make_exponential_temperature(
100, 0.0019),
discount_factor=0.4)
self.name = agentName
def __init__(self, play_with):
super(TicTacToePlayer,
self).__init__(TicTacToeProblem(),
temperature_function=make_exponential_temperature(
1000000, 0.01),
discount_factor=0.4)
self.play_with = play_with
self.other_play_with = 'X' if play_with == 'O' else 'O'
def __init__(self, play_with):
super(TicTacToePlayer, self).__init__(TicTacToeProblem(),
temperature_function=make_exponential_temperature(1000000, 0.01),
discount_factor=0.4)
self.play_with = play_with
self.other_play_with = 'X' if play_with == 'O' else 'O'
def is_completed(self, state):
return state in self.rewards.keys()
def reward(self, state, agent):
return self.rewards.get(state, -0.08)
class WumpusProblem(RLProblem):
def actions(self, state):
actions = ['up', 'down', 'left', 'rigth']
return actions
if __name__ == '__main__':
agent = TDQLearner(WumpusProblem(),
temperature_function=make_exponential_temperature(1000, 0.01),
discount_factor=0.8)
game = WumpusEnvironment(agent)
p = PerformanceCounter([agent], ['Q-learner Agent'])
print 'Training...'
for i in range(10000):
game.run()
p.show_statistics()
game.run(viewer=WumpusViewer(game))
def is_completed(self, state):
return state in self.rewards.keys()
def make_reward(self, state, agent):
return self.rewards.get(state, -0.08)
class GridProblem(RLProblem):
def actions(self, state):
actions = ['up', 'down', 'left', 'rigth']
return actions
if __name__ == '__main__':
agent = TDQLearner(GridProblem(),
temperature_function=make_exponential_temperature(1000, 0.005),
discount_factor=0.9)
game = GridGame(agent)
p = PerformanceCounter([agent], ['Q-learner Agent'])
for i in range(3000):
game.run()
p.show_statistics()
game.run(viewer=Viewer())
def __init__(self, name):
super(TpFinalPlayer, self).__init__(TpFinalProblem(),
temperature_function=make_exponential_temperature(1000000, 0.01),
discount_factor=0.4)
self.name = name
