def _setup_policy(self, time_step_spec, action_spec, boltzmann_temperature,
emit_log_probability):
policy = categorical_q_policy.CategoricalQPolicy(
time_step_spec,
action_spec,
self._q_network,
self._min_q_value,
self._max_q_value,
observation_and_action_constraint_splitter=(
self._observation_and_action_constraint_splitter))
if boltzmann_temperature is not None:
collect_policy = boltzmann_policy.BoltzmannPolicy(
policy, temperature=boltzmann_temperature)
else:
collect_policy = epsilon_greedy_policy.EpsilonGreedyPolicy(
policy, epsilon=self._epsilon_greedy)
policy = greedy_policy.GreedyPolicy(policy)
target_policy = categorical_q_policy.CategoricalQPolicy(
time_step_spec,
action_spec,
self._target_q_network,
self._min_q_value,
self._max_q_value,
observation_and_action_constraint_splitter=(
self._observation_and_action_constraint_splitter))
self._target_greedy_policy = greedy_policy.GreedyPolicy(target_policy)
return policy, collect_policy
def testMasking(self):
batch_size = 1000
num_state_dims = 5
num_actions = 8
observations = tf.random.uniform([batch_size, num_state_dims])
time_step = ts.restart(observations, batch_size=batch_size)
input_tensor_spec = tensor_spec.TensorSpec([num_state_dims], tf.float32)
action_spec = tensor_spec.BoundedTensorSpec(
[1], tf.int32, 0, num_actions - 1)
# We create a fixed mask here for testing purposes. Normally the mask would
# be part of the observation.
mask = [0, 1, 0, 1, 0, 0, 1, 0]
np_mask = np.array(mask)
tf_mask = tf.constant([mask for _ in range(batch_size)])
q_network = categorical_q_network.CategoricalQNetwork(
input_tensor_spec=input_tensor_spec,
action_spec=action_spec,
num_atoms=3,
fc_layer_params=[4])
policy = categorical_q_policy.CategoricalQPolicy(
self._time_step_spec, action_spec, q_network,
self._min_q_value, self._max_q_value,
observation_and_action_constraint_splitter=(
lambda observation: (observation, tf_mask)))
self.evaluate(tf.compat.v1.global_variables_initializer())
# Sample from the policy 1000 times, and ensure that actions considered
# invalid according to the mask are never chosen.
action_step = policy.action(time_step)
action = self.evaluate(action_step.action)
self.assertEqual(action.shape, (batch_size,))
self.assertAllEqual(np_mask[action], np.ones([batch_size]))
def testMasking(self):
batch_size = 1000
num_state_dims = 5
num_actions = 8
observations = tf.random.uniform([batch_size, num_state_dims])
time_step = ts.restart(observations, batch_size=batch_size)
input_tensor_spec = tensor_spec.TensorSpec([num_state_dims], tf.float32)
action_spec = tensor_spec.BoundedTensorSpec(
[1], tf.int32, 0, num_actions - 1)
mask = [0, 1, 0, 1, 0, 0, 1, 0]
np_mask = np.array(mask)
tf_mask = tf.constant([mask for _ in range(batch_size)])
q_network = categorical_q_network.CategoricalQNetwork(
input_tensor_spec=input_tensor_spec,
action_spec=action_spec,
num_atoms=3,
mask_split_fn=lambda observation: (observation, tf_mask),
fc_layer_params=[4])
policy = categorical_q_policy.CategoricalQPolicy(self._min_q_value,
self._max_q_value,
q_network,
action_spec)
# Force creation of variables before global_variables_initializer.
policy.variables()
self.evaluate(tf.compat.v1.global_variables_initializer())
# Sample from the policy 1000 times and ensure that invalid actions are
# never chosen.
action_step = policy.action(time_step)
action = self.evaluate(action_step.action)
self.assertEqual(action.shape, (batch_size,))
self.assertAllEqual(np_mask[action], np.ones([batch_size]))
def testUpdate(self):
policy = categorical_q_policy.CategoricalQPolicy(self._min_q_value,
self._max_q_value,
self._q_network,
self._action_spec)
new_policy = categorical_q_policy.CategoricalQPolicy(self._min_q_value,
self._max_q_value,
self._q_network,
self._action_spec)
observations = tf.constant([[1, 2], [3, 4]], dtype=tf.float32)
time_step = ts.restart(observations)
# There should be two variables in our networks for the fc_layer we
# specified (one kernel and one bias).
self.assertLen(policy.variables(), 2)
self.assertLen(new_policy.variables(), 2)
actions, _, _ = policy.action(time_step)
new_actions, _, _ = new_policy.action(time_step)
self.assertEqual(actions.shape, new_actions.shape)
self.assertEqual(actions.dtype, new_actions.dtype)
self.evaluate(tf.compat.v1.global_variables_initializer())
actions = self.evaluate(actions)
# actions should be a list of two elements; e.g., [0, 1]
self.assertLen(actions, 2)
for action in actions:
self.assertGreaterEqual(action, self._action_spec.minimum)
self.assertLessEqual(action, self._action_spec.maximum)
self.assertEqual(self.evaluate(new_policy.update(policy)), None)
new_actions = self.evaluate(new_actions)
# new_actions should also be a list of two elements; e.g., [0, 1]
self.assertLen(new_actions, 2)
for action in new_actions:
self.assertGreaterEqual(action, self._action_spec.minimum)
self.assertLessEqual(action, self._action_spec.maximum)
def testBuild(self):
policy = categorical_q_policy.CategoricalQPolicy(
self._time_step_spec, self._action_spec, self._q_network,
self._min_q_value, self._max_q_value)
self.assertEqual(policy.time_step_spec, self._time_step_spec)
self.assertEqual(policy.action_spec, self._action_spec)
# There should be two variables in our network for the fc_layer we specified
# (one kernel and one bias).
self.assertLen(policy.variables(), 2)
def testSaver(self):
policy = categorical_q_policy.CategoricalQPolicy(
self._time_step_spec, self._action_spec, self._q_network,
self._min_q_value, self._max_q_value)
saver = policy_saver.PolicySaver(policy)
self.evaluate(tf.compat.v1.global_variables_initializer())
self.evaluate(tf.compat.v1.local_variables_initializer())
save_path = os.path.join(flags.FLAGS.test_tmpdir,
'saved_categorical_q_policy')
saver.save(save_path)
def testMultipleActionsRaiseError(self):
with self.assertRaisesRegexp(
TypeError, '.*action_spec must be a BoundedTensorSpec.*'):
# Replace the action_spec for this test.
action_spec = [tensor_spec.BoundedTensorSpec([1], tf.int32, 0, 1)] * 2
q_network = categorical_q_network.CategoricalQNetwork(
input_tensor_spec=self._obs_spec,
action_spec=action_spec,
num_atoms=3,
fc_layer_params=[4])
categorical_q_policy.CategoricalQPolicy(
self._time_step_spec, action_spec, q_network,
self._min_q_value, self._max_q_value)
def testSaver(self):
policy = categorical_q_policy.CategoricalQPolicy(
self._time_step_spec, self._action_spec, self._q_network,
self._min_q_value, self._max_q_value)
train_step = tf.compat.v1.train.get_or_create_global_step()
saver = policy_saver.PolicySaver(policy, train_step=train_step)
self.evaluate(tf.compat.v1.global_variables_initializer())
self.evaluate(tf.compat.v1.local_variables_initializer())
save_path = os.path.join(flags.FLAGS.test_tmpdir,
'saved_categorical_q_policy')
# For TF1 Compatibility we set the cached session as default. This is a
# no-op in TF2.
with self.cached_session():
saver.save(save_path)
def testSample(self):
policy = categorical_q_policy.CategoricalQPolicy(
self._time_step_spec, self._action_spec, self._q_network,
self._min_q_value, self._max_q_value)
observations = tf.constant([[1, 2], [3, 4]], dtype=tf.float32)
time_step = ts.restart(observations)
actions = policy.action(time_step).action
self.assertEqual(actions.shape.as_list(), [2])
self.assertEqual(actions.dtype, tf.int32)
self.evaluate(tf.compat.v1.global_variables_initializer())
actions = self.evaluate(actions)
# actions should be a list of two elements; e.g., [0, 1]
self.assertLen(actions, 2)
for action in actions:
self.assertGreaterEqual(action, self._action_spec.minimum)
self.assertLessEqual(action, self._action_spec.maximum)
def testMultiSample(self):
policy = categorical_q_policy.CategoricalQPolicy(
self._min_q_value, self._max_q_value, self._q_network,
self._action_spec)
observations = tf.constant([[1, 2], [3, 4]], dtype=tf.float32)
time_step = ts.restart(observations)
actions, _ = policy.step(time_step, num_samples=2)
self.assertEqual(actions.shape.as_list(), [2, 2])
self.assertEqual(actions.dtype, tf.int32)
self.evaluate(tf.compat.v1.global_variables_initializer())
actions = self.evaluate(actions)
# actions should be a nested list of the form [[0, 1], [1, 0]]
self.assertLen(actions, 2)
for inner_list in actions:
self.assertLen(inner_list, 2)
for action in inner_list:
self.assertGreaterEqual(action, self._action_spec.minimum)
self.assertLessEqual(action, self._action_spec.maximum)
def __init__(
self,
time_step_spec,
action_spec,
categorical_q_network,
optimizer,
min_q_value=-10.0,
max_q_value=10.0,
epsilon_greedy=0.1,
n_step_update=1,
boltzmann_temperature=None,
# Params for target network updates
target_update_tau=1.0,
target_update_period=1,
# Params for training.
td_errors_loss_fn=None,
gamma=1.0,
reward_scale_factor=1.0,
gradient_clipping=None,
# Params for debugging
debug_summaries=False,
summarize_grads_and_vars=False,
train_step_counter=None,
name=None):
"""Creates a Categorical DQN Agent.
Args:
time_step_spec: A `TimeStep` spec of the expected time_steps.
action_spec: A `BoundedTensorSpec` representing the actions.
categorical_q_network: A categorical_q_network.CategoricalQNetwork that
returns the q_distribution for each action.
optimizer: The optimizer to use for training.
min_q_value: A float specifying the minimum Q-value, used for setting up
the support.
max_q_value: A float specifying the maximum Q-value, used for setting up
the support.
epsilon_greedy: probability of choosing a random action in the default
epsilon-greedy collect policy (used only if a wrapper is not provided to
the collect_policy method).
n_step_update: The number of steps to consider when computing TD error and
TD loss. Defaults to single-step updates. Note that this requires the
user to call train on Trajectory objects with a time dimension of
`n_step_update + 1`. However, note that we do not yet support
`n_step_update > 1` in the case of RNNs (i.e., non-empty
`q_network.state_spec`).
boltzmann_temperature: Temperature value to use for Boltzmann sampling of
the actions during data collection. The closer to 0.0, the higher the
probability of choosing the best action.
target_update_tau: Factor for soft update of the target networks.
target_update_period: Period for soft update of the target networks.
td_errors_loss_fn: A function for computing the TD errors loss. If None, a
default value of element_wise_huber_loss is used. This function takes as
input the target and the estimated Q values and returns the loss for
each element of the batch.
gamma: A discount factor for future rewards.
reward_scale_factor: Multiplicative scale for the reward.
gradient_clipping: Norm length to clip gradients.
debug_summaries: A bool to gather debug summaries.
summarize_grads_and_vars: If True, gradient and network variable summaries
will be written during training.
train_step_counter: An optional counter to increment every time the train
op is run. Defaults to the global_step.
name: The name of this agent. All variables in this module will fall
under that name. Defaults to the class name.
Raises:
TypeError: If the action spec contains more than one action.
"""
num_atoms = getattr(categorical_q_network, 'num_atoms', None)
if num_atoms is None:
raise TypeError(
'Expected categorical_q_network to have property '
'`num_atoms`, but it doesn\'t (note: you likely want to '
'use a CategoricalQNetwork). Network is: %s' %
(categorical_q_network, ))
self._num_atoms = num_atoms
self._min_q_value = min_q_value
self._max_q_value = max_q_value
self._support = tf.linspace(min_q_value, max_q_value, num_atoms)
super(CategoricalDqnAgent,
self).__init__(time_step_spec,
action_spec,
categorical_q_network,
optimizer,
epsilon_greedy=epsilon_greedy,
n_step_update=n_step_update,
boltzmann_temperature=boltzmann_temperature,
target_update_tau=target_update_tau,
target_update_period=target_update_period,
td_errors_loss_fn=td_errors_loss_fn,
gamma=gamma,
reward_scale_factor=reward_scale_factor,
gradient_clipping=gradient_clipping,
debug_summaries=debug_summaries,
summarize_grads_and_vars=summarize_grads_and_vars,
train_step_counter=train_step_counter,
name=name)
policy = categorical_q_policy.CategoricalQPolicy(
min_q_value, max_q_value, self._q_network, self._action_spec)
if boltzmann_temperature is not None:
self._collect_policy = boltzmann_policy.BoltzmannPolicy(
policy, temperature=self._boltzmann_temperature)
else:
self._collect_policy = epsilon_greedy_policy.EpsilonGreedyPolicy(
policy, epsilon=self._epsilon_greedy)
self._policy = greedy_policy.GreedyPolicy(policy)
def __init__(
self,
time_step_spec,
action_spec,
categorical_q_network,
optimizer,
min_q_value=-10.0,
max_q_value=10.0,
epsilon_greedy=0.1,
n_step_update=1,
boltzmann_temperature=None,
# Params for target network updates
target_categorical_q_network=None,
target_update_tau=1.0,
target_update_period=1,
# Params for training.
td_errors_loss_fn=None,
gamma=1.0,
reward_scale_factor=1.0,
gradient_clipping=None,
# Params for debugging
debug_summaries=False,
summarize_grads_and_vars=False,
train_step_counter=None,
name=None):
"""Creates a Categorical DQN Agent.
Args:
time_step_spec: A `TimeStep` spec of the expected time_steps.
action_spec: A `BoundedTensorSpec` representing the actions.
categorical_q_network: A categorical_q_network.CategoricalQNetwork that
returns the q_distribution for each action.
optimizer: The optimizer to use for training.
min_q_value: A float specifying the minimum Q-value, used for setting up
the support.
max_q_value: A float specifying the maximum Q-value, used for setting up
the support.
epsilon_greedy: probability of choosing a random action in the default
epsilon-greedy collect policy (used only if a wrapper is not provided to
the collect_policy method).
n_step_update: The number of steps to consider when computing TD error and
TD loss. Defaults to single-step updates. Note that this requires the
user to call train on Trajectory objects with a time dimension of
`n_step_update + 1`. However, note that we do not yet support
`n_step_update > 1` in the case of RNNs (i.e., non-empty
`q_network.state_spec`).
boltzmann_temperature: Temperature value to use for Boltzmann sampling of
the actions during data collection. The closer to 0.0, the higher the
probability of choosing the best action.
target_categorical_q_network: (Optional.) A `tf_agents.network.Network`
to be used as the target network during Q learning. Every
`target_update_period` train steps, the weights from
`categorical_q_network` are copied (possibly with smoothing via
`target_update_tau`) to `target_categorical_q_network`.
If `target_categorical_q_network` is not provided, it is created by
making a copy of `categorical_q_network`, which initializes a new
network with the same structure and its own layers and weights.
Network copying is performed via the `Network.copy` superclass method,
and may inadvertently lead to the resulting network to share weights
with the original. This can happen if, for example, the original
network accepted a pre-built Keras layer in its `__init__`, or
accepted a Keras layer that wasn't built, but neglected to create
a new copy.
In these cases, it is up to you to provide a target Network having
weights that are not shared with the original `categorical_q_network`.
If you provide a `target_categorical_q_network` that shares any
weights with `categorical_q_network`, a warning will be logged but
no exception is thrown.
Note; shallow copies of Keras layers may be built via the code:
```python
new_layer = type(layer).from_config(layer.get_config())
```
target_update_tau: Factor for soft update of the target networks.
target_update_period: Period for soft update of the target networks.
td_errors_loss_fn: A function for computing the TD errors loss. If None, a
default value of huber_loss is used. This function takes as input the
target and the estimated Q values and returns the loss for each element
of the batch.
gamma: A discount factor for future rewards.
reward_scale_factor: Multiplicative scale for the reward.
gradient_clipping: Norm length to clip gradients.
debug_summaries: A bool to gather debug summaries.
summarize_grads_and_vars: If True, gradient and network variable summaries
will be written during training.
train_step_counter: An optional counter to increment every time the train
op is run. Defaults to the global_step.
name: The name of this agent. All variables in this module will fall
under that name. Defaults to the class name.
Raises:
TypeError: If the action spec contains more than one action.
"""
super(CategoricalDqnAgent,
self).__init__(time_step_spec,
action_spec,
categorical_q_network,
optimizer,
epsilon_greedy=epsilon_greedy,
n_step_update=n_step_update,
boltzmann_temperature=boltzmann_temperature,
target_q_network=target_categorical_q_network,
target_update_tau=target_update_tau,
target_update_period=target_update_period,
td_errors_loss_fn=td_errors_loss_fn,
gamma=gamma,
reward_scale_factor=reward_scale_factor,
gradient_clipping=gradient_clipping,
debug_summaries=debug_summaries,
summarize_grads_and_vars=summarize_grads_and_vars,
train_step_counter=train_step_counter,
name=name)
def check_atoms(net, label):
num_atoms = getattr(net, 'num_atoms', None)
if num_atoms is None:
raise TypeError(
'Expected {} to have property `num_atoms`, but it '
'doesn\'t (note: you likely want to use a '
'CategoricalQNetwork). Network is: {}'.format(label, net))
return num_atoms
num_atoms = check_atoms(self._q_network, 'categorical_q_network')
target_num_atoms = check_atoms(self._target_q_network,
'target_categorical_q_network')
if num_atoms != target_num_atoms:
raise ValueError(
'categorical_q_network and target_categorical_q_network have '
'different numbers of atoms: {} vs. {}'.format(
num_atoms, target_num_atoms))
self._num_atoms = num_atoms
self._min_q_value = min_q_value
self._max_q_value = max_q_value
self._support = tf.linspace(min_q_value, max_q_value, num_atoms)
policy = categorical_q_policy.CategoricalQPolicy(
min_q_value, max_q_value, self._q_network, self._action_spec)
if boltzmann_temperature is not None:
self._collect_policy = boltzmann_policy.BoltzmannPolicy(
policy, temperature=self._boltzmann_temperature)
else:
self._collect_policy = epsilon_greedy_policy.EpsilonGreedyPolicy(
policy, epsilon=self._epsilon_greedy)
self._policy = greedy_policy.GreedyPolicy(policy)
target_policy = categorical_q_policy.CategoricalQPolicy(
min_q_value, max_q_value, self._target_q_network,
self._action_spec)
self._target_greedy_policy = greedy_policy.GreedyPolicy(target_policy)
