def testCombineTransitions(self):
states, actions, rewards, new_states, reward_mask = (
base.Brain.CombineTransitions([
base.Transition(
s=numpy.array([[1, 2, 3]]),
a=numpy.array([[0, 1, 0]]),
r=1.0,
sp=numpy.array([[4, 5, 6]]),
),
base.Transition(
s=numpy.array([[4, 5, 6]]),
a=numpy.array([[0, 0, 1]]),
r=-1.0,
sp=None,
),
]))
numpy_util.TestUtil.AssertArrayEqual(
numpy.array([[1, 2, 3], [4, 5, 6]]), states)
numpy_util.TestUtil.AssertArrayEqual(
numpy.array([[0, 1, 0], [0, 0, 1]]), actions)
numpy_util.TestUtil.AssertArrayEqual(
numpy.array([1.0, -1.0]), rewards)
numpy_util.TestUtil.AssertArrayEqual(
numpy.array([[4, 5, 6], [4, 5, 6]]), new_states)
numpy_util.TestUtil.AssertArrayEqual(
numpy.array([1, 0]), reward_mask)
def test_convergence(self):
a3c = a3c_impl.A3C(
model=a3c_impl.CreateModel(
state_shape=(3, ),
action_space_size=2,
hidden_layer_sizes=(3, ),
),
# optimizer=a3c_impl.CreateDefaultOptimizer(learning_rate=0.05),
)
s = numpy.array([[1, 2, 3]])
a1 = numpy.array([[1, 0]])
a2 = numpy.array([[0, 1]])
for _ in range(10):
# Needs to train for both actions as one step, otherwise it shows some
# "staggering" effect.
a3c.UpdateFromTransitions([
base.Transition(s=s, a=a1, r=1.0, sp=None),
])
a3c.UpdateFromTransitions([
base.Transition(s=s, a=a2, r=-1.0, sp=s),
])
logging.printf('%s', a3c.GetValues(s))
old_value_a1 = a3c.GetActionValues(a3c.GetValues(s), a1)
# Trains for one step, for both actions.
a3c.UpdateFromTransitions([
base.Transition(s=s, a=a1, r=1.0, sp=None),
])
a3c.UpdateFromTransitions([
base.Transition(s=s, a=a2, r=-1.0, sp=s),
])
self.assertGreaterEqual(a3c.GetActionValues(a3c.GetValues(s), a1),
old_value_a1)
def test_runUsesNewStateAfterIteration(self):
self.env.TakeAction.side_effect = [
base.Transition(
s=numpy.array([[0]]),
a=numpy.array([[0]]),
r=1.0,
sp=numpy.array([[1]]),
),
base.Transition(
s=numpy.array([[1]]),
a=numpy.array([[0]]),
r=1.0,
sp=None,
)
]
self.runner.Run(
env=self.env,
brain=self.qfunc,
policy=self.policy,
num_of_episodes=1,
)
# Tests that the second call is with the new state 1.
self.policy.Decide.assert_called_with(
env=mock.ANY, brain=mock.ANY, state=numpy.array([[1]]),
episode_idx=0, num_of_episodes=1)
def test_UpdateValues_singleTransition(self):
self.qfunc._protected_SetValues(
numpy.array([
[1, 2, 3],
[4, 5, 6],
[2, 2, 2],
]),
numpy.array([
[0.5, 0.5],
[0.3, 0.7],
[0.8, 0.9],
]))
self.qfunc.UpdateFromTransitions([base.Transition(
s=numpy.array([[1, 2, 3]]),
a=numpy.array([[0, 1]]),
r=1.0,
sp=numpy.array([[2, 2, 2]]),
)])
# The new values for state (1,2,3) should be:
# - action (1,0): 0.5, since it's not changed.
# - action (0,1): max(0.8, 0.9) * 0.5 + 1.0 = 1.45
numpy_util.TestUtil.AssertArrayEqual(
numpy.array([[0.5, 1.45]]),
self.qfunc.GetValues(numpy.array([[1, 2, 3]])))
def test_learningRate(self):
# Disables learning from Q* to simplifies testing.
qfunc = qfunc_impl.MemoizationQFunction(
action_space_size=2,
discount_factor=0.0,
learning_rate=0.9,
)
qfunc._protected_SetValues(
numpy.array([
[1, 2, 3],
[4, 5, 6],
]),
numpy.array([
[0.5, 0.6],
[0.3, 0.7],
]))
qfunc.UpdateFromTransitions([base.Transition(
s=numpy.array([[1, 2, 3]]),
a=numpy.array([[0, 1]]),
r=1.0,
sp=numpy.array([[2, 2, 2]]),
)])
# The new values for state (1,2,3) should be:
# - action (1,0): 0.5, since it's not changed.
# - action (0,1): (1-0.9) * 0.6 + 0.9 * 1.0 = 0.96.
numpy_util.TestUtil.AssertArrayEqual(
numpy.array([[0.5, 0.96]]),
qfunc.GetValues(numpy.array([[1, 2, 3]])))
def TakeAction(self, action: base.Action) -> base.Transition:
current_state = self._current_state
move = self.GetChoiceFromAction(action) - 1 # -1, 0, 1
new_state = current_state + move
r = None
if move == 0:
r = 0
else:
if move == 1 and current_state < 0:
r = 1
elif move == -1 and current_state > 0:
r = 1
else:
r = -1
if new_state > self._size:
new_state = -self._size
elif new_state < -self._size:
new_state = self._size
s = numpy.array([[current_state]])
a = action
if self._num_actions_taken >= STEP_LIMIT:
sp = None
else:
sp = numpy.array([[new_state]])
self._current_state = new_state
self._num_actions_taken += 1
return base.Transition(s, a, r, sp)
def test_saveLoad(self):
a3c = a3c_impl.A3C(model=a3c_impl.CreateModel(
state_shape=(3, ),
action_space_size=2,
hidden_layer_sizes=(3, ),
), )
tmp_file = tempfile.NamedTemporaryFile().name
s = numpy.array([[1, 2, 3]])
for _ in range(10):
a3c.UpdateFromTransitions([
base.Transition(s=s,
a=numpy.array([[1, 0]]),
r=1.0,
sp=numpy.array([[4, 5, 6]])),
])
a3c.Save(tmp_file)
saved_values = a3c.GetValues(s)
a3c = a3c_impl.A3C(model=a3c_impl.CreateModel(
state_shape=(3, ),
action_space_size=2,
hidden_layer_sizes=(3, ),
), )
a3c.Load(tmp_file)
numpy_util.TestUtil.AssertArrayEqual(saved_values, a3c.GetValues(s))
def train_push(self, s, a, r, s_):
return self._brain.UpdateFromTransitions([
base.Transition(s=numpy.array([s]),
a=numpy.array([a]),
r=r,
sp=numpy.array([s_]))
])
def setUp(self) -> None:
self.brain = mock.MagicMock()
self.runner = runner_impl.NStepExperienceRunner(
discount_factor=0.5,
n_step_return=5,
)
self.tran = base.Transition(
s=numpy.array([[0]]),
a=numpy.array([[1]]),
r=1.0,
sp=numpy.array([[0]]),
)
def test_memoryManagement(self):
qfunc = qfunc_impl.RandomQFunction(action_space_size=2)
runner = runner_impl.ExperienceReplayRunner(
experience_capacity=1,
experience_sample_batch_size=1,
train_every_n_steps=1)
tran1 = base.Transition(s=numpy.array([[1, 2]]),
a=numpy.array([[1, 0]]),
r=1,
sp=numpy.array([[3, 4]]))
tran2 = base.Transition(s=numpy.array([[3, 4]]),
a=numpy.array([[0, 1]]),
r=1,
sp=numpy.array([[5, 6]]))
runner._protected_ProcessTransition(qfunc, tran1, 0)
runner._protected_ProcessTransition(qfunc, tran2, 1)
hist = runner._experience._history
self.assertEqual(1, len(hist))
self.assertEqual(tran2, hist[0])
def testCalculateNStepReward_whenDone(self):
for _ in range(4):
self.runner._protected_ProcessTransition(self.brain, self.tran, 0)
self.assertFalse(self.brain.called)
tran = base.Transition(
s=numpy.array([[0]]),
a=numpy.array([[1]]),
r=1.0,
sp=None,
)
self.runner._protected_ProcessTransition(self.brain, tran, 0)
rewards = []
for tran in self.brain.UpdateFromTransitions.call_args[0][0]:
rewards.append(tran.r)
self.assertCountEqual([
1.0, 1.0 + 0.5, 1.0 + 0.5 + 0.5**2, 1.0 + 0.5 + 0.5**2 + 0.5**3,
1.0 + 0.5 + 0.5**2 + 0.5**3 + 0.5**4
], rewards)
def _GetNStepTransition(self) -> base.Transition:
# This implementation takes 3.542e-06 sec per call.
R = 0.0
next_discount_factor = 1.0
for tran in self._memory:
R += tran.r * next_discount_factor
next_discount_factor *= self._gamma
# The commented implementation takes 7.322e-06 sec per call.
# rewards = numpy.zeros(self._n_step_return)
# for idx, tran in enumerate(self._memory):
# rewards[idx] = tran.r
# R = numpy.sum(self._gamma_powers * rewards)
return base.Transition(
s=self._memory[0].s,
a=self._memory[0].a,
r=R,
sp=self._memory[-1].sp,
)
def test_UpdateValues_environmentDone(self):
self.qfunc._protected_SetValues(
numpy.array([
[1, 2, 3],
[4, 5, 6],
]),
numpy.array([
[0.5, 0.5],
[0.3, 0.7],
]))
self.qfunc.UpdateFromTransitions([base.Transition(
s=numpy.array([[1, 2, 3]]),
a=numpy.array([[0, 1]]),
r=1.0,
sp=None,
)])
# The new values for state (1,2,3) should be:
# - action (1,0): 0.5, since it's not changed.
# - action (0,1): 1.0, since environment is done, only reward is used.
numpy_util.TestUtil.AssertArrayEqual(
numpy.array([[0.5, 1.0]]),
self.qfunc.GetValues(numpy.array([[1, 2, 3]])))
