def testInfoSpec(self):
PolicyInfo = collections.namedtuple( # pylint: disable=invalid-name
'PolicyInfo',
('log_probability', 'predicted_rewards', 'bandit_policy_type'))
# Set default empty tuple for all fields.
PolicyInfo.__new__.__defaults__ = ((), ) * len(PolicyInfo._fields)
info_spec = PolicyInfo(
bandit_policy_type=self._bandit_policy_type_spec,
log_probability=tensor_spec.BoundedTensorSpec(
shape=(),
dtype=tf.float32,
maximum=0,
minimum=-float('inf'),
name='log_probability'))
policy_with_info_spec = fixed_policy.FixedPolicy(
np.asarray(self._greedy_action, dtype=np.int32),
self._time_step_spec,
self._action_spec,
policy_info=PolicyInfo(
bandit_policy_type=self._bandit_policy_type),
info_spec=info_spec)
epsilon = 0.2
policy = epsilon_greedy_policy.EpsilonGreedyPolicy(
policy_with_info_spec, epsilon=epsilon)
self.assertEqual(policy.time_step_spec, self._time_step_spec)
self.assertEqual(policy.action_spec, self._action_spec)
time_step = tf.nest.map_structure(tf.convert_to_tensor,
self._time_step)
@common.function
def action_step_fn(time_step=time_step):
return policy.action(time_step, policy_state=(), seed=54)
tf.nest.assert_same_structure(
self._action_spec,
self.evaluate(action_step_fn(time_step)).action)
if tf.executing_eagerly():
action_step = action_step_fn
else:
action_step = action_step_fn()
step = self.evaluate(action_step)
tf.nest.assert_same_structure(info_spec, step.info)
self.checkBanditPolicyTypeShape(step.info.bandit_policy_type,
batch_size=2)
def setUp(self):
super(EpsilonGreedyPolicyTest, self).setUp()
self._obs_spec = tensor_spec.TensorSpec([2], tf.float32)
self._time_step_spec = ts.time_step_spec(self._obs_spec)
self._num_actions = 3
self._greedy_action = 1
self._action_spec = tensor_spec.BoundedTensorSpec((1,), tf.int32, 0,
self._num_actions-1)
self._policy = fixed_policy.FixedPolicy(
np.asarray([self._greedy_action], dtype=np.int32),
self._time_step_spec,
self._action_spec)
observations = tf.constant([[1, 2], [3, 4]], dtype=tf.float32)
self._time_step = ts.restart(observations, batch_size=2)
def setUp(self):
super(FixedPolicyTest, self).setUp()
# Creates an MDP with:
# - dim(observation) = 2
# - number of actions = 4
self._obs_spec = tensor_spec.TensorSpec([2], tf.float32)
self._time_step_spec = ts.time_step_spec(self._obs_spec)
self._num_actions = 4
self._action_spec = tensor_spec.BoundedTensorSpec(
shape=(1,), dtype=tf.int32,
minimum=0, maximum=self._num_actions - 1)
# The policy always outputs the same action.
self._fixed_action = 1
self._policy = fixed_policy.FixedPolicy(
np.asarray([self._fixed_action], dtype=np.int32),
self._time_step_spec,
self._action_spec)
def testInfoSpec(self):
PolicyInfo = collections.namedtuple( # pylint: disable=invalid-name
'PolicyInfo', ('log_probability', 'predicted_rewards'))
# Set default empty tuple for all fields.
PolicyInfo.__new__.__defaults__ = ((), ) * len(PolicyInfo._fields)
info_spec = PolicyInfo()
policy_with_info_spec = fixed_policy.FixedPolicy(
np.asarray([self._greedy_action], dtype=np.int32),
self._time_step_spec,
self._action_spec,
policy_info=PolicyInfo(),
info_spec=info_spec)
epsilon = 0.2
policy = epsilon_greedy_policy.EpsilonGreedyPolicy(
policy_with_info_spec, epsilon=epsilon)
self.assertEqual(policy.time_step_spec, self._time_step_spec)
self.assertEqual(policy.action_spec, self._action_spec)
time_step = tf.nest.map_structure(tf.convert_to_tensor,
self._time_step)
@common.function
def action_step_fn(time_step=time_step):
return policy.action(time_step, policy_state=(), seed=54)
tf.nest.assert_same_structure(
self._action_spec,
self.evaluate(action_step_fn(time_step)).action)
if tf.executing_eagerly():
action_step = action_step_fn
else:
action_step = action_step_fn()
step = self.evaluate(action_step)
tf.nest.assert_same_structure(info_spec, step.info)
