def setUp(self) -> None:
self.env = environment_impl.SingleStateEnvironment(
action_space_size=1, step_limit=10)
self.qfunc = qfunc_impl.MemoizationQFunction(
action_space_size=3,
discount_factor=0.9,
learning_rate=0.9)
self.policy = policy_impl.GreedyPolicy()
def setUp(self) -> None:
self.env = environment_impl.SingleStateEnvironment(
action_space_size=1, step_limit=10)
self.qfunc = qfunc_impl.MemoizationQFunction(
action_space_size=3,
discount_factor=0.9,
learning_rate=0.9)
self.runner = runner_impl.SimpleRunner()
def test_stepLimit(self):
env = environment_impl.SingleStateEnvironment(action_space_size=1,
step_limit=10)
env.Reset()
transition = None
for _ in range(10):
transition = env.TakeAction(numpy.array([[0]]))
self.assertIsNotNone(transition.sp)
transition = env.TakeAction(numpy.array([[0]]))
self.assertIsNone(transition.sp)
def test_GreedyPolicy_choosesOptimalAction(self):
mock_qfunc = mock.MagicMock()
mock_qfunc.GetValues.return_value = numpy.array([[0.3, 0.7]])
policy = policy_impl.GreedyPolicy()
self.assertArrayEq(
numpy.array([[0, 1]]),
policy.Decide(env=environment_impl.SingleStateEnvironment(
action_space_size=2, step_limit=10),
brain=mock_qfunc,
state=numpy.array([[0]]),
episode_idx=0,
num_of_episodes=500))
def test_GreedyPolicyWithRandomness_choosesNonOptimalAction(self):
mock_qfunc = mock.MagicMock()
mock_qfunc.GetValues.return_value = numpy.array([[0.3, 0.7]])
policy = policy_impl.GreedyPolicyWithRandomness(epsilon=0.5)
for _ in range(500):
policy.Decide(env=environment_impl.SingleStateEnvironment(
action_space_size=2, step_limit=10),
brain=mock_qfunc,
state=numpy.array([[0]]),
episode_idx=0,
num_of_episodes=500)
# Tests that roughly half of the time qfunc is not used to make the
# decision.
self.assertGreater(mock_qfunc.GetValues.call_count, 200)
self.assertLess(mock_qfunc.GetValues.call_count, 300)
def test_GreedyPolicyWithDecreasingRandomness_epsilonDecays(self):
mock_qfunc = mock.MagicMock()
mock_qfunc.GetValues.return_value = numpy.array([[0.3, 0.7]])
env = environment_impl.SingleStateEnvironment(action_space_size=2,
step_limit=10)
policy = policy_impl.GreedyPolicyWithDecreasingRandomness(
initial_epsilon=1.0,
final_epsilon=0.0,
decay_by_half_after_num_of_episodes=50)
# When episode_idx is 0, the action should be purely random.
choices = []
for _ in range(500):
choices.append(
env.GetChoiceFromAction(
policy.Decide(env=env,
brain=mock_qfunc,
state=numpy.array([[0]]),
episode_idx=0,
num_of_episodes=500)))
# Tests that roughly half of the time action 0 is chosen.
num_of_0s = len([c for c in choices if c == 0])
self.assertGreater(num_of_0s, 200)
self.assertLess(num_of_0s, 300)
# When episode_idx is really large, the action should be close to optimal.
choices = []
for _ in range(500):
choices.append(
env.GetChoiceFromAction(
policy.Decide(env=env,
brain=mock_qfunc,
state=numpy.array([[0]]),
episode_idx=500,
num_of_episodes=500)))
# Tests that almost all the time the optimal action 1 is chosen.
num_of_0s = len([c for c in choices if c == 0])
self.assertLess(num_of_0s, 5)
def test_GreedyPolicyWithRandomness_considerAllActions(self):
mock_qfunc = mock.MagicMock()
mock_qfunc.GetValues.return_value = numpy.array([[0.3, 0.7]])
env = environment_impl.SingleStateEnvironment(action_space_size=2,
step_limit=10)
policy = policy_impl.GreedyPolicyWithRandomness(epsilon=1.0)
choices = []
for _ in range(500):
choices.append(
env.GetChoiceFromAction(
policy.Decide(env=env,
brain=mock_qfunc,
state=numpy.array([[0]]),
episode_idx=0,
num_of_episodes=500)))
# Tests that roughly half of the time action 0 is chosen.
num_of_0s = len([c for c in choices if c == 0])
self.assertGreater(num_of_0s, 200)
self.assertLess(num_of_0s, 300)
