def test_epsilon_greedy(self):
self.action_value[self.mock_state1, self.mock_action1] = 1.
self.action_value[self.mock_state1, self.mock_action2] = 2.
self.action_value[self.mock_state1, self.mock_action3] = 3.
self.assertEqual(
self.policy.epsilon_greedy(self.mock_state1,
MockActionSpace({
self.mock_action1,
self.mock_action2, self.mock_action3
}),
epsilon=0), MockAction([5, 6, 7]))
self.action_value[MockState([1, 2, 3, 4, 5]), self.mock_action2] = 10.
self.assertEqual(
self.policy.epsilon_greedy(self.mock_state1,
MockActionSpace({
self.mock_action1,
self.mock_action2, self.mock_action3
}),
epsilon=0), MockAction([2, 3, 4]))
self.assertEqual(
self.policy.epsilon_greedy(self.mock_state1,
MockActionSpace({self.mock_action1}),
epsilon=0), MockAction([1, 2, 3]))
self.assertEqual(
self.policy.epsilon_greedy(self.mock_state2,
MockActionSpace({self.mock_action3}),
epsilon=0), MockAction([5, 6, 7]))
def setUp(self):
self.action_value = LinearRegressionActionValue(
init_weights_zeros=True)
self.mock_state1 = MockState(
np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]]))
self.mock_action1 = MockAction(1, 2)
self.mock_action2 = MockAction(5, 6)
def setUp(self):
self.action_value = LazyTabularActionValue()
self.mock_state1 = MockState(np.array([1, 2, 3, 4, 5]))
self.mock_state2 = MockState(np.array([6, 5, 4, 3]))
self.mock_action1 = MockAction(1, 2)
self.mock_action2 = MockAction(5, 6)
self.mock_action3 = MockAction(7, 8)
self.mock_state1_copy = MockState(np.array([1, 2, 3, 4, 5]))
def setUp(self):
self.action_value = LazyTabularActionValue()
self.policy = ActionValueDerivedPolicy(self.action_value)
self.mock_state1 = MockState([1, 2, 3, 4, 5])
self.mock_state2 = MockState([6, 5, 4, 3])
self.mock_action1 = MockAction([1, 2, 3])
self.mock_action2 = MockAction([2, 3, 4])
self.mock_action3 = MockAction([5, 6, 7])
self.mock_action4 = MockAction([5, 6, 8])
self.mock_state1_copy = MockState([1, 2, 3, 4, 5])
return graph
def _repr_svg_(self):
return self._get_graph()._repr_svg_()
def view(self):
return self._get_graph().view()
if __name__ == '__main__':
# Model test
m = StochasticModel()
s1 = MockState([[-1, 0], [-1, -1]])
a1 = MockAction([1, 0])
s2 = MockState([[-1, 0], [0, -1]])
s3 = MockState([[-1, 0], [-1, 0]])
m[s1, a1] = s2
m[s1, a1] = s3
m[s1, a1] = s3
m[s1, a1] = s3
m[s1, a1] = s3
a2 = MockAction([0, 0])
s4 = MockState([[0, 0], [0, -1]])
m[s2, a2] = s4
a4 = MockAction([1, 1])
s5 = MockState([[-1, 0], [0, 0]])
s6 = MockState([[-1, 0], [-1, 0]])
str(hash(greedy_action)),
label=str(greedy_action_value),
color="red",
penwidth="2")
return graph
def _repr_svg_(self):
return self._get_graph()._repr_svg_()
def view(self):
return self._get_graph().view()
if __name__ == '__main__':
av = LazyTabularActionValue()
s = MockState([[-1, -1], [-1, 1]])
a1 = MockAction([0, 0])
a2 = MockAction([0, 1])
a3 = MockAction([1, 0])
av[s, a1] = 6
av[s, a2] = 2.9
av[s, a3] = -10
egp = ActionValueDerivedPolicy(av)
print(egp)
# egp.view()