class ReinforcedController(Controller):
NAME = "Reinforced Controller"
def __init__(self, pendulum_length, pendulum_mass, cart_mass):
super(ReinforcedController, self).__init__(pendulum_length,
pendulum_mass, cart_mass)
self.ranges = self.get_ranges()
self.model = InvertedPendulumModel(self.pendulum_length,
self.pendulum_mass, self.cart_mass)
self.force_granularity = 2
self.environment = CartEnvironment(
self.model, *self.ranges, force_granularity=self.force_granularity)
# self.load()
def learn(self, number_of_iterations):
learner = Q(0.2, 0.8)
task = CartMovingTask(self.environment)
self.controller = ActionValueTable(
reduce(lambda x, y: x * y, map(lambda x: len(x), self.ranges)),
self.force_granularity)
self.controller.initialize(1.)
agent = LearningAgent(self.controller, learner)
experiment = Experiment(task, agent)
for i in range(number_of_iterations):
experiment.doInteractions(1)
agent.learn()
agent.reset()
with open("test.pcl", "w+") as f:
pickle.dump(self.controller, f)
def load(self):
with open("test.pcl", "r+") as f:
self.controller = pickle.load(f)
def calculate_force(self, angular_position, angular_velocity,
cart_position, cart_velocity):
state = self.environment.normalized_state(
(angular_position, angular_velocity, cart_position, cart_velocity))
action = self.controller.getMaxAction(state)
force = self.environment.allActions[action]
return force
def get_ranges(self):
cart_position_ranges = [(-1000000000, -2.4), (-2.4, -0.8), (-0.8, 0.8),
(0.8, 2.4), (2.4, 1000000000)]
cart_velocity_ranges = [(-1000000000, -0.5), (-0.5, 0.5),
(0.5, 1000000000)]
angles_ranges = [(-6.28, -1), (-1, 0), (0, 1), (1, 6.28)]
angular_velocity_ranges = [(-1000000000, -3.14), (-3.14, 3.14),
(3.14, 1000000000)]
return (cart_position_ranges, cart_velocity_ranges, angles_ranges,
angular_velocity_ranges)
class ReinforcedController(Controller):
NAME = "Reinforced Controller"
def __init__(self, pendulum_length, pendulum_mass, cart_mass):
super(ReinforcedController, self).__init__(pendulum_length, pendulum_mass, cart_mass)
self.ranges = self.get_ranges()
self.model = InvertedPendulumModel(self.pendulum_length, self.pendulum_mass, self.cart_mass)
self.force_granularity = 2
self.environment = CartEnvironment(self.model, *self.ranges, force_granularity=self.force_granularity)
# self.load()
def learn(self, number_of_iterations):
learner = Q(0.2, 0.8)
task = CartMovingTask(self.environment)
self.controller = ActionValueTable(
reduce(lambda x, y: x * y, map(lambda x: len(x), self.ranges)), self.force_granularity
)
self.controller.initialize(1.0)
agent = LearningAgent(self.controller, learner)
experiment = Experiment(task, agent)
for i in range(number_of_iterations):
experiment.doInteractions(1)
agent.learn()
agent.reset()
with open("test.pcl", "w+") as f:
pickle.dump(self.controller, f)
def load(self):
with open("test.pcl", "r+") as f:
self.controller = pickle.load(f)
def calculate_force(self, angular_position, angular_velocity, cart_position, cart_velocity):
state = self.environment.normalized_state((angular_position, angular_velocity, cart_position, cart_velocity))
action = self.controller.getMaxAction(state)
force = self.environment.allActions[action]
return force
def get_ranges(self):
cart_position_ranges = [(-1000000000, -2.4), (-2.4, -0.8), (-0.8, 0.8), (0.8, 2.4), (2.4, 1000000000)]
cart_velocity_ranges = [(-1000000000, -0.5), (-0.5, 0.5), (0.5, 1000000000)]
angles_ranges = [(-6.28, -1), (-1, 0), (0, 1), (1, 6.28)]
angular_velocity_ranges = [(-1000000000, -3.14), (-3.14, 3.14), (3.14, 1000000000)]
return (cart_position_ranges, cart_velocity_ranges, angles_ranges, angular_velocity_ranges)
