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def _critic_loss_with_optional_entropy_term(
self,
time_steps: ts.TimeStep,
actions: types.Tensor,
next_time_steps: ts.TimeStep,
td_errors_loss_fn: types.LossFn,
gamma: types.Float = 1.0,
reward_scale_factor: types.Float = 1.0,
weights: Optional[types.Tensor] = None,
... | Computes the critic loss for CQL-SAC training.
The original SAC critic loss is:
```
(q(s, a) - (r(s, a) + \gamma q(s', a') - \gamma \alpha \log \pi(a'|s')))^2
```
The CQL-SAC critic loss makes the entropy term optional.
CQL may value unseen actions higher since it lower-bounds the value of
... | _critic_loss_with_optional_entropy_term | python | tensorflow/agents | tf_agents/agents/cql/cql_sac_agent.py | https://github.com/tensorflow/agents/blob/master/tf_agents/agents/cql/cql_sac_agent.py | Apache-2.0 |
def __init__(
self,
input_tensor_spec,
output_tensor_spec,
fc_layer_params=None,
dropout_layer_params=None,
conv_layer_params=None,
activation_fn=tf.keras.activations.relu,
kernel_initializer=None,
last_kernel_initializer=None,
name='ActorNetwork',
):
""... | Creates an instance of `ActorNetwork`.
Args:
input_tensor_spec: A nest of `tensor_spec.TensorSpec` representing the
inputs.
output_tensor_spec: A nest of `tensor_spec.BoundedTensorSpec` representing
the outputs.
fc_layer_params: Optional list of fully_connected parameters, where e... | __init__ | python | tensorflow/agents | tf_agents/agents/ddpg/actor_network.py | https://github.com/tensorflow/agents/blob/master/tf_agents/agents/ddpg/actor_network.py | Apache-2.0 |
def __init__(
self,
input_tensor_spec,
output_tensor_spec,
conv_layer_params=None,
input_fc_layer_params=(200, 100),
lstm_size=(40,),
output_fc_layer_params=(200, 100),
activation_fn=tf.keras.activations.relu,
name='ActorRnnNetwork',
):
"""Creates an instance ... | Creates an instance of `ActorRnnNetwork`.
Args:
input_tensor_spec: A nest of `tensor_spec.TensorSpec` representing the
input observations.
output_tensor_spec: A nest of `tensor_spec.BoundedTensorSpec` representing
the actions.
conv_layer_params: Optional list of convolution layers... | __init__ | python | tensorflow/agents | tf_agents/agents/ddpg/actor_rnn_network.py | https://github.com/tensorflow/agents/blob/master/tf_agents/agents/ddpg/actor_rnn_network.py | Apache-2.0 |
def __init__(
self,
input_tensor_spec,
observation_conv_layer_params=None,
observation_fc_layer_params=None,
observation_dropout_layer_params=None,
action_fc_layer_params=None,
action_dropout_layer_params=None,
joint_fc_layer_params=None,
joint_dropout_layer_params=... | Creates an instance of `CriticNetwork`.
Args:
input_tensor_spec: A tuple of (observation, action) each a nest of
`tensor_spec.TensorSpec` representing the inputs.
observation_conv_layer_params: Optional list of convolution layer
parameters for observations, where each item is a length-t... | __init__ | python | tensorflow/agents | tf_agents/agents/ddpg/critic_network.py | https://github.com/tensorflow/agents/blob/master/tf_agents/agents/ddpg/critic_network.py | Apache-2.0 |
def __init__(
self,
input_tensor_spec,
observation_conv_layer_params=None,
observation_fc_layer_params=(200,),
action_fc_layer_params=(200,),
joint_fc_layer_params=(100,),
lstm_size=None,
output_fc_layer_params=(200, 100),
activation_fn=tf.keras.activations.relu,
... | Creates an instance of `CriticRnnNetwork`.
Args:
input_tensor_spec: A tuple of (observation, action) each of type
`tensor_spec.TensorSpec` representing the inputs.
observation_conv_layer_params: Optional list of convolution layers
parameters to apply to the observations, where each item... | __init__ | python | tensorflow/agents | tf_agents/agents/ddpg/critic_rnn_network.py | https://github.com/tensorflow/agents/blob/master/tf_agents/agents/ddpg/critic_rnn_network.py | Apache-2.0 |
def __init__(
self,
time_step_spec: ts.TimeStep,
action_spec: types.NestedTensorSpec,
actor_network: network.Network,
critic_network: network.Network,
actor_optimizer: Optional[types.Optimizer] = None,
critic_optimizer: Optional[types.Optimizer] = None,
ou_stddev: types.F... | Creates a DDPG Agent.
Args:
time_step_spec: A `TimeStep` spec of the expected time_steps.
action_spec: A nest of BoundedTensorSpec representing the actions.
actor_network: A tf_agents.network.Network to be used by the agent. The
network will be called with call(observation, step_type[, po... | __init__ | python | tensorflow/agents | tf_agents/agents/ddpg/ddpg_agent.py | https://github.com/tensorflow/agents/blob/master/tf_agents/agents/ddpg/ddpg_agent.py | Apache-2.0 |
def _get_target_updater(self, tau=1.0, period=1):
"""Performs a soft update of the target network parameters.
For each weight w_s in the original network, and its corresponding
weight w_t in the target network, a soft update is:
w_t = (1- tau) x w_t + tau x ws
Args:
tau: A float scalar in [0... | Performs a soft update of the target network parameters.
For each weight w_s in the original network, and its corresponding
weight w_t in the target network, a soft update is:
w_t = (1- tau) x w_t + tau x ws
Args:
tau: A float scalar in [0, 1]. Default `tau=1.0` means hard update.
period: ... | _get_target_updater | python | tensorflow/agents | tf_agents/agents/ddpg/ddpg_agent.py | https://github.com/tensorflow/agents/blob/master/tf_agents/agents/ddpg/ddpg_agent.py | Apache-2.0 |
def critic_loss(
self,
time_steps: ts.TimeStep,
actions: types.NestedTensor,
next_time_steps: ts.TimeStep,
weights: Optional[types.Tensor] = None,
training: bool = False,
) -> types.Tensor:
"""Computes the critic loss for DDPG training.
Args:
time_steps: A batch of t... | Computes the critic loss for DDPG training.
Args:
time_steps: A batch of timesteps.
actions: A batch of actions.
next_time_steps: A batch of next timesteps.
weights: Optional scalar or element-wise (per-batch-entry) importance
weights.
training: Whether this loss is being used... | critic_loss | python | tensorflow/agents | tf_agents/agents/ddpg/ddpg_agent.py | https://github.com/tensorflow/agents/blob/master/tf_agents/agents/ddpg/ddpg_agent.py | Apache-2.0 |
def actor_loss(
self,
time_steps: ts.TimeStep,
weights: Optional[types.Tensor] = None,
training: bool = False,
) -> types.Tensor:
"""Computes the actor_loss for DDPG training.
Args:
time_steps: A batch of timesteps.
weights: Optional scalar or element-wise (per-batch-entry... | Computes the actor_loss for DDPG training.
Args:
time_steps: A batch of timesteps.
weights: Optional scalar or element-wise (per-batch-entry) importance
weights.
training: Whether this loss is being used for training.
Returns:
actor_loss: A scalar actor loss.
| actor_loss | python | tensorflow/agents | tf_agents/agents/ddpg/ddpg_agent.py | https://github.com/tensorflow/agents/blob/master/tf_agents/agents/ddpg/ddpg_agent.py | Apache-2.0 |
def train_eval(
root_dir,
env_name='HalfCheetah-v2',
eval_env_name=None,
env_load_fn=suite_mujoco.load,
num_iterations=2000000,
actor_fc_layers=(400, 300),
critic_obs_fc_layers=(400,),
critic_action_fc_layers=None,
critic_joint_fc_layers=(300,),
# Params for collect
initial_c... | A simple train and eval for DDPG. | train_eval | python | tensorflow/agents | tf_agents/agents/ddpg/examples/v2/train_eval.py | https://github.com/tensorflow/agents/blob/master/tf_agents/agents/ddpg/examples/v2/train_eval.py | Apache-2.0 |
def create_actor_network(fc_layer_units, action_spec):
"""Create an actor network for DDPG."""
flat_action_spec = tf.nest.flatten(action_spec)
if len(flat_action_spec) > 1:
raise ValueError('Only a single action tensor is supported by this network')
flat_action_spec = flat_action_spec[0]
fc_layers = [den... | Create an actor network for DDPG. | create_actor_network | python | tensorflow/agents | tf_agents/agents/ddpg/examples/v2/train_eval.py | https://github.com/tensorflow/agents/blob/master/tf_agents/agents/ddpg/examples/v2/train_eval.py | Apache-2.0 |
def _check_network_output(self, net, label):
"""Check outputs of q_net and target_q_net against expected shape.
Subclasses that require different q_network outputs should override
this function.
Args:
net: A `Network`.
label: A label to print in case of a mismatch.
"""
outputs = ne... | Check outputs of q_net and target_q_net against expected shape.
Subclasses that require different q_network outputs should override
this function.
Args:
net: A `Network`.
label: A label to print in case of a mismatch.
| _check_network_output | python | tensorflow/agents | tf_agents/agents/dqn/dqn_agent.py | https://github.com/tensorflow/agents/blob/master/tf_agents/agents/dqn/dqn_agent.py | Apache-2.0 |
def _get_target_updater(self, tau=1.0, period=1):
"""Performs a soft update of the target network parameters.
For each weight w_s in the q network, and its corresponding
weight w_t in the target_q_network, a soft update is:
w_t = (1 - tau) * w_t + tau * w_s
Args:
tau: A float scalar in [0, 1... | Performs a soft update of the target network parameters.
For each weight w_s in the q network, and its corresponding
weight w_t in the target_q_network, a soft update is:
w_t = (1 - tau) * w_t + tau * w_s
Args:
tau: A float scalar in [0, 1]. Default `tau=1.0` means hard update.
period: Ste... | _get_target_updater | python | tensorflow/agents | tf_agents/agents/dqn/dqn_agent.py | https://github.com/tensorflow/agents/blob/master/tf_agents/agents/dqn/dqn_agent.py | Apache-2.0 |
def _loss(
self,
experience,
td_errors_loss_fn=None,
gamma=1.0,
reward_scale_factor=1.0,
weights=None,
training=False,
):
"""Computes loss for DQN training.
Args:
experience: A batch of experience data in the form of a `Trajectory` or
`Transition`. The ... | Computes loss for DQN training.
Args:
experience: A batch of experience data in the form of a `Trajectory` or
`Transition`. The structure of `experience` must match that of
`self.collect_policy.step_spec`. If a `Trajectory`, all tensors in
`experience` must be shaped `[B, T, ...]` wh... | _loss | python | tensorflow/agents | tf_agents/agents/dqn/dqn_agent.py | https://github.com/tensorflow/agents/blob/master/tf_agents/agents/dqn/dqn_agent.py | Apache-2.0 |
def _compute_next_q_values(self, next_time_steps, info):
"""Compute the q value of the next state for TD error computation.
Args:
next_time_steps: A batch of next timesteps
info: PolicyStep.info that may be used by other agents inherited from
dqn_agent.
Returns:
A tensor of Q val... | Compute the q value of the next state for TD error computation.
Args:
next_time_steps: A batch of next timesteps
info: PolicyStep.info that may be used by other agents inherited from
dqn_agent.
Returns:
A tensor of Q values for the given next state.
| _compute_next_q_values | python | tensorflow/agents | tf_agents/agents/dqn/dqn_agent.py | https://github.com/tensorflow/agents/blob/master/tf_agents/agents/dqn/dqn_agent.py | Apache-2.0 |
def testLossNStepMidMidLastFirst(self, agent_class):
"""Tests that n-step loss handles LAST time steps properly."""
q_net = DummyNet(self._observation_spec, self._action_spec)
agent = agent_class(
self._time_step_spec,
self._action_spec,
q_network=q_net,
optimizer=None,
... | Tests that n-step loss handles LAST time steps properly. | testLossNStepMidMidLastFirst | python | tensorflow/agents | tf_agents/agents/dqn/dqn_agent_test.py | https://github.com/tensorflow/agents/blob/master/tf_agents/agents/dqn/dqn_agent_test.py | Apache-2.0 |
def train_eval(
root_dir,
env_name='CartPole-v0',
num_iterations=100000,
train_sequence_length=1,
# Params for QNetwork
fc_layer_params=(100,),
# Params for QRnnNetwork
input_fc_layer_params=(50,),
lstm_size=(20,),
output_fc_layer_params=(20,),
# Params for collect
initia... | A simple train and eval for DQN. | train_eval | python | tensorflow/agents | tf_agents/agents/dqn/examples/v2/train_eval.py | https://github.com/tensorflow/agents/blob/master/tf_agents/agents/dqn/examples/v2/train_eval.py | Apache-2.0 |
def tanh_and_scale_to_spec(inputs, spec):
"""Maps inputs with arbitrary range to range defined by spec using `tanh`."""
means = (spec.maximum + spec.minimum) / 2.0
magnitudes = (spec.maximum - spec.minimum) / 2.0
return means + magnitudes * tf.tanh(inputs) | Maps inputs with arbitrary range to range defined by spec using `tanh`. | tanh_and_scale_to_spec | python | tensorflow/agents | tf_agents/agents/ppo/ppo_actor_network.py | https://github.com/tensorflow/agents/blob/master/tf_agents/agents/ppo/ppo_actor_network.py | Apache-2.0 |
def create_sequential_actor_net(
self, fc_layer_units, action_tensor_spec, seed=None
):
"""Helper method for creating the actor network."""
self._seed_stream = self.seed_stream_class(
seed=seed, salt='tf_agents_sequential_layers'
)
def _get_seed():
seed = self._seed_stream()
... | Helper method for creating the actor network. | create_sequential_actor_net | python | tensorflow/agents | tf_agents/agents/ppo/ppo_actor_network.py | https://github.com/tensorflow/agents/blob/master/tf_agents/agents/ppo/ppo_actor_network.py | Apache-2.0 |
def __init__(
self,
time_step_spec: ts.TimeStep,
action_spec: types.NestedTensorSpec,
optimizer: Optional[types.Optimizer] = None,
actor_net: Optional[network.Network] = None,
value_net: Optional[network.Network] = None,
greedy_eval: bool = True,
importance_ratio_clipping... | Creates a PPO Agent.
Args:
time_step_spec: A `TimeStep` spec of the expected time_steps.
action_spec: A nest of `BoundedTensorSpec` representing the actions.
optimizer: Optimizer to use for the agent, default to using
`tf.compat.v1.train.AdamOptimizer`.
actor_net: A `network.Distrib... | __init__ | python | tensorflow/agents | tf_agents/agents/ppo/ppo_agent.py | https://github.com/tensorflow/agents/blob/master/tf_agents/agents/ppo/ppo_agent.py | Apache-2.0 |
def compute_advantages(
self,
rewards: types.NestedTensor,
returns: types.Tensor,
discounts: types.Tensor,
value_preds: types.Tensor,
) -> types.Tensor:
"""Compute advantages, optionally using GAE.
Based on baselines ppo1 implementation. Removes final timestep, as it needs
t... | Compute advantages, optionally using GAE.
Based on baselines ppo1 implementation. Removes final timestep, as it needs
to use this timestep for next-step value prediction for TD error
computation.
Args:
rewards: Tensor of per-timestep rewards.
returns: Tensor of per-timestep returns.
... | compute_advantages | python | tensorflow/agents | tf_agents/agents/ppo/ppo_agent.py | https://github.com/tensorflow/agents/blob/master/tf_agents/agents/ppo/ppo_agent.py | Apache-2.0 |
def get_loss(
self,
time_steps: ts.TimeStep,
actions: types.NestedTensorSpec,
act_log_probs: types.Tensor,
returns: types.Tensor,
normalized_advantages: types.Tensor,
action_distribution_parameters: types.NestedTensor,
weights: types.Tensor,
train_step: tf.Variable,... | Compute the loss and create optimization op for one training epoch.
All tensors should have a single batch dimension.
Args:
time_steps: A minibatch of TimeStep tuples.
actions: A minibatch of actions.
act_log_probs: A minibatch of action probabilities (probability under the
sampling ... | get_loss | python | tensorflow/agents | tf_agents/agents/ppo/ppo_agent.py | https://github.com/tensorflow/agents/blob/master/tf_agents/agents/ppo/ppo_agent.py | Apache-2.0 |
def compute_return_and_advantage(
self, next_time_steps: ts.TimeStep, value_preds: types.Tensor
) -> Tuple[types.Tensor, types.Tensor]:
"""Compute the Monte Carlo return and advantage.
Args:
next_time_steps: batched tensor of TimeStep tuples after action is taken.
value_preds: Batched value... | Compute the Monte Carlo return and advantage.
Args:
next_time_steps: batched tensor of TimeStep tuples after action is taken.
value_preds: Batched value prediction tensor. Should have one more entry
in time index than time_steps, with the final value corresponding to the
value predictio... | compute_return_and_advantage | python | tensorflow/agents | tf_agents/agents/ppo/ppo_agent.py | https://github.com/tensorflow/agents/blob/master/tf_agents/agents/ppo/ppo_agent.py | Apache-2.0 |
def _preprocess(self, experience):
"""Performs advantage calculation for the collected experience.
Args:
experience: A (batch of) experience in the form of a `Trajectory`. The
structure of `experience` must match that of `self.collect_data_spec`.
All tensors in `experience` must be shaped... | Performs advantage calculation for the collected experience.
Args:
experience: A (batch of) experience in the form of a `Trajectory`. The
structure of `experience` must match that of `self.collect_data_spec`.
All tensors in `experience` must be shaped `[batch, time + 1, ...]` or
[time... | _preprocess | python | tensorflow/agents | tf_agents/agents/ppo/ppo_agent.py | https://github.com/tensorflow/agents/blob/master/tf_agents/agents/ppo/ppo_agent.py | Apache-2.0 |
def _preprocess_sequence(self, experience):
"""Performs advantage calculation for the collected experience.
This function is a no-op if self._compute_value_and_advantage_in_train is
True, which means advantage calculation happens as part of agent.train().
Args:
experience: A (batch of) experienc... | Performs advantage calculation for the collected experience.
This function is a no-op if self._compute_value_and_advantage_in_train is
True, which means advantage calculation happens as part of agent.train().
Args:
experience: A (batch of) experience in the form of a `Trajectory`. The
struct... | _preprocess_sequence | python | tensorflow/agents | tf_agents/agents/ppo/ppo_agent.py | https://github.com/tensorflow/agents/blob/master/tf_agents/agents/ppo/ppo_agent.py | Apache-2.0 |
def entropy_regularization_loss(
self,
time_steps: ts.TimeStep,
entropy: types.Tensor,
weights: types.Tensor,
debug_summaries: bool = False,
) -> types.Tensor:
"""Create regularization loss tensor based on agent parameters."""
if self._entropy_regularization > 0:
nest_utils... | Create regularization loss tensor based on agent parameters. | entropy_regularization_loss | python | tensorflow/agents | tf_agents/agents/ppo/ppo_agent.py | https://github.com/tensorflow/agents/blob/master/tf_agents/agents/ppo/ppo_agent.py | Apache-2.0 |
def value_estimation_loss(
self,
time_steps: ts.TimeStep,
returns: types.Tensor,
weights: types.Tensor,
old_value_predictions: Optional[types.Tensor] = None,
debug_summaries: bool = False,
training: bool = False,
) -> types.Tensor:
"""Computes the value estimation loss fo... | Computes the value estimation loss for actor-critic training.
All tensors should have a single batch dimension.
Args:
time_steps: A batch of timesteps.
returns: Per-timestep returns for value function to predict. (Should come
from TD-lambda computation.)
weights: Optional scalar or e... | value_estimation_loss | python | tensorflow/agents | tf_agents/agents/ppo/ppo_agent.py | https://github.com/tensorflow/agents/blob/master/tf_agents/agents/ppo/ppo_agent.py | Apache-2.0 |
def policy_gradient_loss(
self,
time_steps: ts.TimeStep,
actions: types.NestedTensor,
sample_action_log_probs: types.Tensor,
advantages: types.Tensor,
current_policy_distribution: types.NestedDistribution,
weights: types.Tensor,
debug_summaries: bool = False,
) -> types... | Create tensor for policy gradient loss.
All tensors should have a single batch dimension.
Args:
time_steps: TimeSteps with observations for each timestep.
actions: Tensor of actions for timesteps, aligned on index.
sample_action_log_probs: Tensor of sample probability of each action.
a... | policy_gradient_loss | python | tensorflow/agents | tf_agents/agents/ppo/ppo_agent.py | https://github.com/tensorflow/agents/blob/master/tf_agents/agents/ppo/ppo_agent.py | Apache-2.0 |
def kl_penalty_loss(
self,
time_steps: ts.TimeStep,
action_distribution_parameters: types.NestedTensor,
current_policy_distribution: types.NestedDistribution,
weights: types.Tensor,
debug_summaries: bool = False,
) -> types.Tensor:
"""Compute a loss that penalizes policy steps ... | Compute a loss that penalizes policy steps with high KL.
Based on KL divergence from old (data-collection) policy to new (updated)
policy.
All tensors should have a single batch dimension.
Args:
time_steps: TimeStep tuples with observations for each timestep. Used for
computing new acti... | kl_penalty_loss | python | tensorflow/agents | tf_agents/agents/ppo/ppo_agent.py | https://github.com/tensorflow/agents/blob/master/tf_agents/agents/ppo/ppo_agent.py | Apache-2.0 |
def update_adaptive_kl_beta(
self, kl_divergence: types.Tensor
) -> Optional[tf.Operation]:
"""Create update op for adaptive KL penalty coefficient.
Args:
kl_divergence: KL divergence of old policy to new policy for all
timesteps.
Returns:
update_op: An op which runs the update... | Create update op for adaptive KL penalty coefficient.
Args:
kl_divergence: KL divergence of old policy to new policy for all
timesteps.
Returns:
update_op: An op which runs the update for the adaptive kl penalty term.
| update_adaptive_kl_beta | python | tensorflow/agents | tf_agents/agents/ppo/ppo_agent.py | https://github.com/tensorflow/agents/blob/master/tf_agents/agents/ppo/ppo_agent.py | Apache-2.0 |
def _get_discount(experience) -> types.Tensor:
"""Try to get the discount entry from `experience`.
Typically experience is either a Trajectory or a Transition.
Args:
experience: Data collected from e.g. a replay buffer.
Returns:
discount: The discount tensor stored in `experience`.
"""
if isinsta... | Try to get the discount entry from `experience`.
Typically experience is either a Trajectory or a Transition.
Args:
experience: Data collected from e.g. a replay buffer.
Returns:
discount: The discount tensor stored in `experience`.
| _get_discount | python | tensorflow/agents | tf_agents/agents/ppo/ppo_agent.py | https://github.com/tensorflow/agents/blob/master/tf_agents/agents/ppo/ppo_agent.py | Apache-2.0 |
def _compute_returns_fn(rewards, discounts, next_state_return=0.0):
"""Python implementation of computing discounted returns."""
returns = np.zeros_like(rewards)
for t in range(len(returns) - 1, -1, -1):
returns[t] = rewards[t] + discounts[t] * next_state_return
next_state_return = returns[t]
return ret... | Python implementation of computing discounted returns. | _compute_returns_fn | python | tensorflow/agents | tf_agents/agents/ppo/ppo_agent_test.py | https://github.com/tensorflow/agents/blob/master/tf_agents/agents/ppo/ppo_agent_test.py | Apache-2.0 |
def __init__(
self,
time_step_spec: ts.TimeStep,
action_spec: types.NestedTensorSpec,
optimizer: Optional[types.Optimizer] = None,
actor_net: Optional[network.Network] = None,
value_net: Optional[network.Network] = None,
greedy_eval: bool = True,
importance_ratio_clipping... | Creates a PPO Agent implementing the clipped probability ratios.
Args:
time_step_spec: A `TimeStep` spec of the expected time_steps.
action_spec: A nest of BoundedTensorSpec representing the actions.
optimizer: Optimizer to use for the agent.
actor_net: A function actor_net(observations, ac... | __init__ | python | tensorflow/agents | tf_agents/agents/ppo/ppo_clip_agent.py | https://github.com/tensorflow/agents/blob/master/tf_agents/agents/ppo/ppo_clip_agent.py | Apache-2.0 |
def __init__(
self,
time_step_spec: ts.TimeStep,
action_spec: types.NestedTensorSpec,
actor_net: network.Network,
value_net: network.Network,
num_epochs: int,
initial_adaptive_kl_beta: types.Float,
adaptive_kl_target: types.Float,
adaptive_kl_tolerance: types.Float,... | Creates a PPO Agent implementing the KL penalty loss.
Args:
time_step_spec: A `TimeStep` spec of the expected time_steps.
action_spec: A nest of `BoundedTensorSpec` representing the actions.
actor_net: A `network.DistributionNetwork` which maps observations to
action distributions. Common... | __init__ | python | tensorflow/agents | tf_agents/agents/ppo/ppo_kl_penalty_agent.py | https://github.com/tensorflow/agents/blob/master/tf_agents/agents/ppo/ppo_kl_penalty_agent.py | Apache-2.0 |
def get_initial_value_state(
self, batch_size: types.Int
) -> types.NestedTensor:
"""Returns the initial state of the value network.
Args:
batch_size: A constant or Tensor holding the batch size. Can be None, in
which case the state will not have a batch dimension added.
Returns:
... | Returns the initial state of the value network.
Args:
batch_size: A constant or Tensor holding the batch size. Can be None, in
which case the state will not have a batch dimension added.
Returns:
A nest of zero tensors matching the spec of the value network state.
| get_initial_value_state | python | tensorflow/agents | tf_agents/agents/ppo/ppo_policy.py | https://github.com/tensorflow/agents/blob/master/tf_agents/agents/ppo/ppo_policy.py | Apache-2.0 |
def apply_value_network(
self,
observations: types.NestedTensor,
step_types: types.Tensor,
value_state: Optional[types.NestedTensor] = None,
training: bool = False,
) -> types.NestedTensor:
"""Apply value network to time_step, potentially a sequence.
If observation_normalizer is... | Apply value network to time_step, potentially a sequence.
If observation_normalizer is not None, applies observation normalization.
Args:
observations: A (possibly nested) observation tensor with outer_dims
either (batch_size,) or (batch_size, time_index). If observations is a
time serie... | apply_value_network | python | tensorflow/agents | tf_agents/agents/ppo/ppo_policy.py | https://github.com/tensorflow/agents/blob/master/tf_agents/agents/ppo/ppo_policy.py | Apache-2.0 |
def make_trajectory_mask(batched_traj: trajectory.Trajectory) -> types.Tensor:
"""Mask boundary trajectories and those with invalid returns and advantages.
Args:
batched_traj: Trajectory, doubly-batched [batch_dim, time_dim,...]. It must
be preprocessed already.
Returns:
A mask, type tf.float32, t... | Mask boundary trajectories and those with invalid returns and advantages.
Args:
batched_traj: Trajectory, doubly-batched [batch_dim, time_dim,...]. It must
be preprocessed already.
Returns:
A mask, type tf.float32, that is 0.0 for all between-episode Trajectory
(batched_traj.step_type is LAST)... | make_trajectory_mask | python | tensorflow/agents | tf_agents/agents/ppo/ppo_utils.py | https://github.com/tensorflow/agents/blob/master/tf_agents/agents/ppo/ppo_utils.py | Apache-2.0 |
def make_timestep_mask(
batched_next_time_step: ts.TimeStep, allow_partial_episodes: bool = False
) -> types.Tensor:
"""Create a mask for transitions and optionally final incomplete episodes.
Args:
batched_next_time_step: Next timestep, doubly-batched [batch_dim, time_dim,
...].
allow_partial_epi... | Create a mask for transitions and optionally final incomplete episodes.
Args:
batched_next_time_step: Next timestep, doubly-batched [batch_dim, time_dim,
...].
allow_partial_episodes: If true, then steps on incomplete episodes are
allowed.
Returns:
A mask, type tf.float32, that is 0.0 for ... | make_timestep_mask | python | tensorflow/agents | tf_agents/agents/ppo/ppo_utils.py | https://github.com/tensorflow/agents/blob/master/tf_agents/agents/ppo/ppo_utils.py | Apache-2.0 |
def nested_kl_divergence(
nested_from_distribution: types.NestedDistribution,
nested_to_distribution: types.NestedDistribution,
outer_dims: Sequence[int] = (),
) -> types.Tensor:
"""Given two nested distributions, sum the KL divergences of the leaves."""
nest_utils.assert_same_structure(
nested_fr... | Given two nested distributions, sum the KL divergences of the leaves. | nested_kl_divergence | python | tensorflow/agents | tf_agents/agents/ppo/ppo_utils.py | https://github.com/tensorflow/agents/blob/master/tf_agents/agents/ppo/ppo_utils.py | Apache-2.0 |
def get_metric_observers(metrics):
"""Returns a list of observers, one for each metric."""
def get_metric_observer(metric):
def metric_observer(time_step, action, next_time_step, policy_state):
action_step = policy_step.PolicyStep(action, policy_state, ())
traj = trajectory.from_transition(time_st... | Returns a list of observers, one for each metric. | get_metric_observers | python | tensorflow/agents | tf_agents/agents/ppo/ppo_utils.py | https://github.com/tensorflow/agents/blob/master/tf_agents/agents/ppo/ppo_utils.py | Apache-2.0 |
def get_learning_rate(optimizer):
"""Gets the current learning rate from an optimizer to be graphed."""
# Keras optimizers uses `learning_rate`.
if hasattr(optimizer, 'learning_rate'):
learning_rate = optimizer.learning_rate # pylint: disable=protected-access
# Adam optimizers store their learning rate in ... | Gets the current learning rate from an optimizer to be graphed. | get_learning_rate | python | tensorflow/agents | tf_agents/agents/ppo/ppo_utils.py | https://github.com/tensorflow/agents/blob/master/tf_agents/agents/ppo/ppo_utils.py | Apache-2.0 |
def train_eval(
root_dir,
env_name='HalfCheetah-v2',
env_load_fn=suite_mujoco.load,
random_seed=None,
# TODO(b/127576522): rename to policy_fc_layers.
actor_fc_layers=(200, 100),
value_fc_layers=(200, 100),
use_rnns=False,
lstm_size=(20,),
# Params for collect
num_environment... | A simple train and eval for PPO. | train_eval | python | tensorflow/agents | tf_agents/agents/ppo/examples/v2/train_eval_clip_agent.py | https://github.com/tensorflow/agents/blob/master/tf_agents/agents/ppo/examples/v2/train_eval_clip_agent.py | Apache-2.0 |
def _get_target_updater(self, tau=1.0, period=1):
"""Performs a soft update of the target network.
For each weight w_s in the q network, and its corresponding
weight w_t in the target_q_network, a soft update is:
w_t = (1 - tau) * w_t + tau * w_s
Args:
tau: A float scalar in [0, 1]. Default ... | Performs a soft update of the target network.
For each weight w_s in the q network, and its corresponding
weight w_t in the target_q_network, a soft update is:
w_t = (1 - tau) * w_t + tau * w_s
Args:
tau: A float scalar in [0, 1]. Default `tau=1.0` means hard update. Used
for target netw... | _get_target_updater | python | tensorflow/agents | tf_agents/agents/qtopt/qtopt_agent.py | https://github.com/tensorflow/agents/blob/master/tf_agents/agents/qtopt/qtopt_agent.py | Apache-2.0 |
def _get_target_updater_delayed(self, tau_delayed=1.0, period_delayed=1):
"""Performs a soft update of the delayed target network.
For each weight w_s in the q network, and its corresponding
weight w_t in the target_q_network, a soft update is:
w_t = (1 - tau) * w_t + tau * w_s
Args:
tau_del... | Performs a soft update of the delayed target network.
For each weight w_s in the q network, and its corresponding
weight w_t in the target_q_network, a soft update is:
w_t = (1 - tau) * w_t + tau * w_s
Args:
tau_delayed: A float scalar in [0, 1]. Default `tau=1.0` means hard
update. Used... | _get_target_updater_delayed | python | tensorflow/agents | tf_agents/agents/qtopt/qtopt_agent.py | https://github.com/tensorflow/agents/blob/master/tf_agents/agents/qtopt/qtopt_agent.py | Apache-2.0 |
def _add_auxiliary_losses(self, transition, weights, losses_dict):
"""Computes auxiliary losses, updating losses_dict in place."""
total_auxiliary_loss = 0
if self._auxiliary_loss_fns is not None:
for auxiliary_loss_fn in self._auxiliary_loss_fns:
auxiliary_loss, auxiliary_reg_loss = auxiliary... | Computes auxiliary losses, updating losses_dict in place. | _add_auxiliary_losses | python | tensorflow/agents | tf_agents/agents/qtopt/qtopt_agent.py | https://github.com/tensorflow/agents/blob/master/tf_agents/agents/qtopt/qtopt_agent.py | Apache-2.0 |
def _loss(self, experience, weights=None, training=False):
"""Computes loss for QtOpt training.
Args:
experience: A batch of experience data in the form of a `Trajectory` or
`Transition`. The structure of `experience` must match that of
`self.collect_policy.step_spec`. If a `Trajectory`,... | Computes loss for QtOpt training.
Args:
experience: A batch of experience data in the form of a `Trajectory` or
`Transition`. The structure of `experience` must match that of
`self.collect_policy.step_spec`. If a `Trajectory`, all tensors in
`experience` must be shaped `[B, T, ...]` ... | _loss | python | tensorflow/agents | tf_agents/agents/qtopt/qtopt_agent.py | https://github.com/tensorflow/agents/blob/master/tf_agents/agents/qtopt/qtopt_agent.py | Apache-2.0 |
def __init__(
self,
time_step_spec: ts.TimeStep,
action_spec: types.NestedTensorSpec,
policy_class: PolicyClassType,
debug_summaries: bool = False,
summarize_grads_and_vars: bool = False,
train_step_counter: Optional[tf.Variable] = None,
num_outer_dims: int = 1,
nam... | Creates a fixed-policy agent with no-op for training.
Args:
time_step_spec: A `TimeStep` spec of the expected time_steps.
action_spec: A nest of BoundedTensorSpec representing the actions.
policy_class: a tf_policy.TFPolicy or py_policy.PyPolicy class to use as a
policy.
debug_summa... | __init__ | python | tensorflow/agents | tf_agents/agents/random/fixed_policy_agent.py | https://github.com/tensorflow/agents/blob/master/tf_agents/agents/random/fixed_policy_agent.py | Apache-2.0 |
def _train(self, experience, weights):
"""Do nothing. Arguments are ignored and loss is always 0."""
del experience # Unused
del weights # Unused
# Incrementing the step counter.
self.train_step_counter.assign_add(1)
# Returning 0 loss.
return tf_agent.LossInfo(0.0, None) | Do nothing. Arguments are ignored and loss is always 0. | _train | python | tensorflow/agents | tf_agents/agents/random/fixed_policy_agent.py | https://github.com/tensorflow/agents/blob/master/tf_agents/agents/random/fixed_policy_agent.py | Apache-2.0 |
def __init__(
self,
time_step_spec: ts.TimeStep,
action_spec: types.NestedTensorSpec,
debug_summaries: bool = False,
summarize_grads_and_vars: bool = False,
train_step_counter: Optional[tf.Variable] = None,
num_outer_dims: int = 1,
name: Optional[Text] = None,
):
""... | Creates a random agent.
Args:
time_step_spec: A `TimeStep` spec of the expected time_steps.
action_spec: A nest of BoundedTensorSpec representing the actions.
debug_summaries: A bool to gather debug summaries.
summarize_grads_and_vars: If true, gradient summaries will be written.
trai... | __init__ | python | tensorflow/agents | tf_agents/agents/random/random_agent.py | https://github.com/tensorflow/agents/blob/master/tf_agents/agents/random/random_agent.py | Apache-2.0 |
def _standard_normalize(values, axes=(0,)):
"""Standard normalizes values `values`.
Args:
values: Tensor with values to be standardized.
axes: Axes used to compute mean and variances.
Returns:
Standardized values (values - mean(values[axes])) / std(values[axes]).
"""
values_mean, values_var = tf... | Standard normalizes values `values`.
Args:
values: Tensor with values to be standardized.
axes: Axes used to compute mean and variances.
Returns:
Standardized values (values - mean(values[axes])) / std(values[axes]).
| _standard_normalize | python | tensorflow/agents | tf_agents/agents/reinforce/reinforce_agent.py | https://github.com/tensorflow/agents/blob/master/tf_agents/agents/reinforce/reinforce_agent.py | Apache-2.0 |
def _entropy_loss(distributions, spec, weights=None):
"""Computes entropy loss.
Args:
distributions: A possibly batched tuple of distributions.
spec: A nested tuple representing the action spec.
weights: Optional scalar or element-wise (per-batch-entry) importance
weights. Includes a mask for in... | Computes entropy loss.
Args:
distributions: A possibly batched tuple of distributions.
spec: A nested tuple representing the action spec.
weights: Optional scalar or element-wise (per-batch-entry) importance
weights. Includes a mask for invalid timesteps.
Returns:
A Tensor representing the ... | _entropy_loss | python | tensorflow/agents | tf_agents/agents/reinforce/reinforce_agent.py | https://github.com/tensorflow/agents/blob/master/tf_agents/agents/reinforce/reinforce_agent.py | Apache-2.0 |
def _get_initial_policy_state(policy, time_steps):
"""Gets the initial state of a policy."""
batch_size = (
tf.compat.dimension_at_index(time_steps.discount.shape, 0)
or tf.shape(time_steps.discount)[0]
)
return policy.get_initial_state(batch_size=batch_size) | Gets the initial state of a policy. | _get_initial_policy_state | python | tensorflow/agents | tf_agents/agents/reinforce/reinforce_agent.py | https://github.com/tensorflow/agents/blob/master/tf_agents/agents/reinforce/reinforce_agent.py | Apache-2.0 |
def __init__(
self,
time_step_spec: ts.TimeStep,
action_spec: types.TensorSpec,
actor_network: network.Network,
optimizer: types.Optimizer,
value_network: Optional[network.Network] = None,
value_estimation_loss_coef: types.Float = 0.2,
advantage_fn: Optional[AdvantageFnTy... | Creates a REINFORCE Agent.
Args:
time_step_spec: A `TimeStep` spec of the expected time_steps.
action_spec: A nest of BoundedTensorSpec representing the actions.
actor_network: A tf_agents.network.Network to be used by the agent. The
network will be called with call(observation, step_type... | __init__ | python | tensorflow/agents | tf_agents/agents/reinforce/reinforce_agent.py | https://github.com/tensorflow/agents/blob/master/tf_agents/agents/reinforce/reinforce_agent.py | Apache-2.0 |
def policy_gradient_loss(
self,
actions_distribution: types.NestedDistribution,
actions: types.NestedTensor,
is_boundary: types.Tensor,
returns: types.Tensor,
num_episodes: types.Int,
weights: Optional[types.Tensor] = None,
) -> types.Tensor:
"""Computes the policy gradie... | Computes the policy gradient loss.
Args:
actions_distribution: A possibly batched tuple of action distributions.
actions: Tensor with a batch of actions.
is_boundary: Tensor of booleans that indicate if the corresponding action
was in a boundary trajectory and should be ignored.
ret... | policy_gradient_loss | python | tensorflow/agents | tf_agents/agents/reinforce/reinforce_agent.py | https://github.com/tensorflow/agents/blob/master/tf_agents/agents/reinforce/reinforce_agent.py | Apache-2.0 |
def entropy_regularization_loss(
self,
actions_distribution: types.NestedDistribution,
weights: Optional[types.Tensor] = None,
) -> types.Tensor:
"""Computes the optional entropy regularization loss.
Extending REINFORCE by entropy regularization was originally proposed in
"Function opti... | Computes the optional entropy regularization loss.
Extending REINFORCE by entropy regularization was originally proposed in
"Function optimization using connectionist reinforcement learning
algorithms." (Williams and Peng, 1991).
Args:
actions_distribution: A possibly batched tuple of action dis... | entropy_regularization_loss | python | tensorflow/agents | tf_agents/agents/reinforce/reinforce_agent.py | https://github.com/tensorflow/agents/blob/master/tf_agents/agents/reinforce/reinforce_agent.py | Apache-2.0 |
def value_estimation_loss(
self,
value_preds: types.Tensor,
returns: types.Tensor,
num_episodes: types.Int,
weights: Optional[types.Tensor] = None,
) -> types.Tensor:
"""Computes the value estimation loss.
Args:
value_preds: Per-timestep estimated values.
returns: Pe... | Computes the value estimation loss.
Args:
value_preds: Per-timestep estimated values.
returns: Per-timestep returns for value function to predict.
num_episodes: Number of episodes contained in the training data.
weights: Optional scalar or element-wise (per-batch-entry) importance
w... | value_estimation_loss | python | tensorflow/agents | tf_agents/agents/reinforce/reinforce_agent.py | https://github.com/tensorflow/agents/blob/master/tf_agents/agents/reinforce/reinforce_agent.py | Apache-2.0 |
def train_eval(
root_dir,
env_name='CartPole-v0',
num_iterations=1000,
actor_fc_layers=(100,),
value_net_fc_layers=(100,),
use_value_network=False,
use_tf_functions=True,
# Params for collect
collect_episodes_per_iteration=2,
replay_buffer_capacity=2000,
# Params for train
... | A simple train and eval for Reinforce. | train_eval | python | tensorflow/agents | tf_agents/agents/reinforce/examples/v2/train_eval.py | https://github.com/tensorflow/agents/blob/master/tf_agents/agents/reinforce/examples/v2/train_eval.py | Apache-2.0 |
def __init__(
self,
time_step_spec: ts.TimeStep,
action_spec: types.NestedTensorSpec,
critic_network: network.Network,
actor_network: network.Network,
actor_optimizer: types.Optimizer,
critic_optimizer: types.Optimizer,
alpha_optimizer: types.Optimizer,
actor_loss_w... | Creates a SAC Agent.
Args:
time_step_spec: A `TimeStep` spec of the expected time_steps.
action_spec: A nest of BoundedTensorSpec representing the actions.
critic_network: A function critic_network((observations, actions)) that
returns the q_values for each observation and action.
a... | __init__ | python | tensorflow/agents | tf_agents/agents/sac/sac_agent.py | https://github.com/tensorflow/agents/blob/master/tf_agents/agents/sac/sac_agent.py | Apache-2.0 |
def _initialize(self):
"""Returns an op to initialize the agent.
Copies weights from the Q networks to the target Q network.
"""
common.soft_variables_update(
self._critic_network_1.variables,
self._target_critic_network_1.variables,
tau=1.0,
)
common.soft_variables_upda... | Returns an op to initialize the agent.
Copies weights from the Q networks to the target Q network.
| _initialize | python | tensorflow/agents | tf_agents/agents/sac/sac_agent.py | https://github.com/tensorflow/agents/blob/master/tf_agents/agents/sac/sac_agent.py | Apache-2.0 |
def _loss(
self,
experience: types.NestedTensor,
weights: Optional[types.Tensor] = None,
training: bool = False,
):
"""Returns the loss of the provided experience.
This method is only used at test time!
Args:
experience: A time-stacked trajectory object.
weights: Opti... | Returns the loss of the provided experience.
This method is only used at test time!
Args:
experience: A time-stacked trajectory object.
weights: Optional scalar or elementwise (per-batch-entry) importance
weights.
training: Whether this loss is being calculated as part of training.
... | _loss | python | tensorflow/agents | tf_agents/agents/sac/sac_agent.py | https://github.com/tensorflow/agents/blob/master/tf_agents/agents/sac/sac_agent.py | Apache-2.0 |
def _get_target_updater(self, tau=1.0, period=1):
"""Performs a soft update of the target network parameters.
For each weight w_s in the original network, and its corresponding
weight w_t in the target network, a soft update is:
w_t = (1- tau) x w_t + tau x ws
Args:
tau: A float scalar in [0... | Performs a soft update of the target network parameters.
For each weight w_s in the original network, and its corresponding
weight w_t in the target network, a soft update is:
w_t = (1- tau) x w_t + tau x ws
Args:
tau: A float scalar in [0, 1]. Default `tau=1.0` means hard update.
period: ... | _get_target_updater | python | tensorflow/agents | tf_agents/agents/sac/sac_agent.py | https://github.com/tensorflow/agents/blob/master/tf_agents/agents/sac/sac_agent.py | Apache-2.0 |
def critic_loss(
self,
time_steps: ts.TimeStep,
actions: types.Tensor,
next_time_steps: ts.TimeStep,
td_errors_loss_fn: types.LossFn,
gamma: types.Float = 1.0,
reward_scale_factor: types.Float = 1.0,
weights: Optional[types.Tensor] = None,
training: bool = False,
... | Computes the critic loss for SAC training.
Args:
time_steps: A batch of timesteps.
actions: A batch of actions.
next_time_steps: A batch of next timesteps.
td_errors_loss_fn: A function(td_targets, predictions) to compute
elementwise (per-batch-entry) loss.
gamma: Discount for... | critic_loss | python | tensorflow/agents | tf_agents/agents/sac/sac_agent.py | https://github.com/tensorflow/agents/blob/master/tf_agents/agents/sac/sac_agent.py | Apache-2.0 |
def actor_loss(
self,
time_steps: ts.TimeStep,
weights: Optional[types.Tensor] = None,
training: Optional[bool] = True,
) -> types.Tensor:
"""Computes the actor_loss for SAC training.
Args:
time_steps: A batch of timesteps.
weights: Optional scalar or elementwise (per-batc... | Computes the actor_loss for SAC training.
Args:
time_steps: A batch of timesteps.
weights: Optional scalar or elementwise (per-batch-entry) importance
weights.
training: Whether training should be applied.
Returns:
actor_loss: A scalar actor loss.
| actor_loss | python | tensorflow/agents | tf_agents/agents/sac/sac_agent.py | https://github.com/tensorflow/agents/blob/master/tf_agents/agents/sac/sac_agent.py | Apache-2.0 |
def alpha_loss(
self,
time_steps: ts.TimeStep,
weights: Optional[types.Tensor] = None,
training: bool = False,
) -> types.Tensor:
"""Computes the alpha_loss for EC-SAC training.
Args:
time_steps: A batch of timesteps.
weights: Optional scalar or elementwise (per-batch-entr... | Computes the alpha_loss for EC-SAC training.
Args:
time_steps: A batch of timesteps.
weights: Optional scalar or elementwise (per-batch-entry) importance
weights.
training: Whether this loss is being used during training.
Returns:
alpha_loss: A scalar alpha loss.
| alpha_loss | python | tensorflow/agents | tf_agents/agents/sac/sac_agent.py | https://github.com/tensorflow/agents/blob/master/tf_agents/agents/sac/sac_agent.py | Apache-2.0 |
def __init__(
self,
sample_spec: types.TensorSpec,
activation_fn: Optional[Callable[[types.Tensor], types.Tensor]] = None,
std_transform: Optional[Callable[[types.Tensor], types.Tensor]] = tf.exp,
name: Text = 'TanhNormalProjectionNetwork',
):
"""Creates an instance of TanhNormalProj... | Creates an instance of TanhNormalProjectionNetwork.
Args:
sample_spec: A `tensor_spec.BoundedTensorSpec` detailing the shape and
dtypes of samples pulled from the output distribution.
activation_fn: Activation function to use in dense layer.
std_transform: Transformation function to apply... | __init__ | python | tensorflow/agents | tf_agents/agents/sac/tanh_normal_projection_network.py | https://github.com/tensorflow/agents/blob/master/tf_agents/agents/sac/tanh_normal_projection_network.py | Apache-2.0 |
def train_eval(
root_dir,
env_name='HalfCheetah-v2',
eval_env_name=None,
env_load_fn=suite_mujoco.load,
# The SAC paper reported:
# Hopper and Cartpole results up to 1000000 iters,
# Humanoid results up to 10000000 iters,
# Other mujoco tasks up to 3000000 iters.
num_iterations=30000... | A simple train and eval for SAC. | train_eval | python | tensorflow/agents | tf_agents/agents/sac/examples/v2/train_eval.py | https://github.com/tensorflow/agents/blob/master/tf_agents/agents/sac/examples/v2/train_eval.py | Apache-2.0 |
def train_eval(
root_dir,
env_name='cartpole',
task_name='balance',
observations_allowlist='position',
eval_env_name=None,
num_iterations=1000000,
# Params for networks.
actor_fc_layers=(400, 300),
actor_output_fc_layers=(100,),
actor_lstm_size=(40,),
critic_obs_fc_layers=Non... | A simple train and eval for RNN SAC on DM control. | train_eval | python | tensorflow/agents | tf_agents/agents/sac/examples/v2/train_eval_rnn.py | https://github.com/tensorflow/agents/blob/master/tf_agents/agents/sac/examples/v2/train_eval_rnn.py | Apache-2.0 |
def __init__(
self,
time_step_spec: ts.TimeStep,
action_spec: types.NestedTensor,
actor_network: network.Network,
critic_network: network.Network,
actor_optimizer: types.Optimizer,
critic_optimizer: types.Optimizer,
exploration_noise_std: types.Float = 0.1,
critic_n... | Creates a Td3Agent Agent.
Args:
time_step_spec: A `TimeStep` spec of the expected time_steps.
action_spec: A nest of BoundedTensorSpec representing the actions.
actor_network: A tf_agents.network.Network to be used by the agent. The
network will be called with call(observation, step_type)... | __init__ | python | tensorflow/agents | tf_agents/agents/td3/td3_agent.py | https://github.com/tensorflow/agents/blob/master/tf_agents/agents/td3/td3_agent.py | Apache-2.0 |
def _initialize(self):
"""Initialize the agent.
Copies weights from the actor and critic networks to the respective
target actor and critic networks.
"""
common.soft_variables_update(
self._critic_network_1.variables,
self._target_critic_network_1.variables,
tau=1.0,
)
... | Initialize the agent.
Copies weights from the actor and critic networks to the respective
target actor and critic networks.
| _initialize | python | tensorflow/agents | tf_agents/agents/td3/td3_agent.py | https://github.com/tensorflow/agents/blob/master/tf_agents/agents/td3/td3_agent.py | Apache-2.0 |
def _get_target_updater(self, tau=1.0, period=1):
"""Performs a soft update of the target network parameters.
For each weight w_s in the original network, and its corresponding
weight w_t in the target network, a soft update is:
w_t = (1- tau) x w_t + tau x ws
Args:
tau: A float scalar in [0... | Performs a soft update of the target network parameters.
For each weight w_s in the original network, and its corresponding
weight w_t in the target network, a soft update is:
w_t = (1- tau) x w_t + tau x ws
Args:
tau: A float scalar in [0, 1]. Default `tau=1.0` means hard update.
period: ... | _get_target_updater | python | tensorflow/agents | tf_agents/agents/td3/td3_agent.py | https://github.com/tensorflow/agents/blob/master/tf_agents/agents/td3/td3_agent.py | Apache-2.0 |
def critic_loss(
self,
time_steps: ts.TimeStep,
actions: types.Tensor,
next_time_steps: ts.TimeStep,
weights: Optional[types.Tensor] = None,
training: bool = False,
) -> types.Tensor:
"""Computes the critic loss for TD3 training.
Args:
time_steps: A batch of timestep... | Computes the critic loss for TD3 training.
Args:
time_steps: A batch of timesteps.
actions: A batch of actions.
next_time_steps: A batch of next timesteps.
weights: Optional scalar or element-wise (per-batch-entry) importance
weights.
training: Whether this loss is being used ... | critic_loss | python | tensorflow/agents | tf_agents/agents/td3/td3_agent.py | https://github.com/tensorflow/agents/blob/master/tf_agents/agents/td3/td3_agent.py | Apache-2.0 |
def actor_loss(
self,
time_steps: ts.TimeStep,
weights: Optional[types.Tensor] = None,
training: bool = False,
) -> types.Tensor:
"""Computes the actor_loss for TD3 training.
Args:
time_steps: A batch of timesteps.
weights: Optional scalar or element-wise (per-batch-entry)... | Computes the actor_loss for TD3 training.
Args:
time_steps: A batch of timesteps.
weights: Optional scalar or element-wise (per-batch-entry) importance
weights.
training: Whether this loss is being used for training.
Returns:
actor_loss: A scalar actor loss.
| actor_loss | python | tensorflow/agents | tf_agents/agents/td3/td3_agent.py | https://github.com/tensorflow/agents/blob/master/tf_agents/agents/td3/td3_agent.py | Apache-2.0 |
def __init__(
self,
num_actions: int,
dtype: tf.DType = tf.float32,
name: Optional[Text] = None,
):
"""Initializes an instance of `BernoulliBanditVariableCollection`.
It creates all the variables needed for `BernoulliThompsonSamplingAgent`.
For each action, the agent maintains the... | Initializes an instance of `BernoulliBanditVariableCollection`.
It creates all the variables needed for `BernoulliThompsonSamplingAgent`.
For each action, the agent maintains the `alpha` and `beta` parameters of
the beta distribution.
Args:
num_actions: (int) The number of actions.
dtype: ... | __init__ | python | tensorflow/agents | tf_agents/bandits/agents/bernoulli_thompson_sampling_agent.py | https://github.com/tensorflow/agents/blob/master/tf_agents/bandits/agents/bernoulli_thompson_sampling_agent.py | Apache-2.0 |
def selective_sum(
values: types.Tensor, partitions: types.Int, num_partitions: int
) -> types.Tensor:
"""Sums entries in `values`, partitioned using `partitions`.
For example,
```python
# Returns `[0 + 4 + 5, 2 + 3 + 4]` i.e. `[9, 6]`.
selective_sum(values=[0, 1, 2, 3, 4, 5],
p... | Sums entries in `values`, partitioned using `partitions`.
For example,
```python
# Returns `[0 + 4 + 5, 2 + 3 + 4]` i.e. `[9, 6]`.
selective_sum(values=[0, 1, 2, 3, 4, 5],
partitions=[0, 1, 1, 1, 0, 0]),
num_partitions=2)
```
Args:
values: a `Tensor` with n... | selective_sum | python | tensorflow/agents | tf_agents/bandits/agents/exp3_agent.py | https://github.com/tensorflow/agents/blob/master/tf_agents/bandits/agents/exp3_agent.py | Apache-2.0 |
def __init__(
self,
time_step_spec: types.TimeStep,
action_spec: types.BoundedTensorSpec,
learning_rate: float,
name: Optional[Text] = None,
):
"""Initialize an instance of `Exp3Agent`.
Args:
time_step_spec: A `TimeStep` spec describing the expected `TimeStep`s.
acti... | Initialize an instance of `Exp3Agent`.
Args:
time_step_spec: A `TimeStep` spec describing the expected `TimeStep`s.
action_spec: A scalar `BoundedTensorSpec` with `int32` or `int64` dtype
describing the number of actions for this agent.
learning_rate: A float valued scalar. A higher value... | __init__ | python | tensorflow/agents | tf_agents/bandits/agents/exp3_agent.py | https://github.com/tensorflow/agents/blob/master/tf_agents/bandits/agents/exp3_agent.py | Apache-2.0 |
def _train(self, experience, weights=None):
"""Updates the policy based on the data in `experience`.
Note that `experience` should only contain data points that this agent has
not previously seen. If `experience` comes from a replay buffer, this buffer
should be cleared between each call to `train`.
... | Updates the policy based on the data in `experience`.
Note that `experience` should only contain data points that this agent has
not previously seen. If `experience` comes from a replay buffer, this buffer
should be cleared between each call to `train`.
Args:
experience: A batch of experience da... | _train | python | tensorflow/agents | tf_agents/bandits/agents/exp3_agent.py | https://github.com/tensorflow/agents/blob/master/tf_agents/bandits/agents/exp3_agent.py | Apache-2.0 |
def testExp3Update(
self,
observation_shape,
num_actions,
action,
log_prob,
reward,
learning_rate,
):
"""Check EXP3 updates for specified actions and rewards."""
# Compute expected update for each action.
expected_update_value = exp3_agent.exp3_update_value(rewar... | Check EXP3 updates for specified actions and rewards. | testExp3Update | python | tensorflow/agents | tf_agents/bandits/agents/exp3_agent_test.py | https://github.com/tensorflow/agents/blob/master/tf_agents/bandits/agents/exp3_agent_test.py | Apache-2.0 |
def __init__(
self,
num_agents: int,
reward_aggregates: Optional[List[float]] = None,
inverse_temperature: float = 0.0,
):
"""Initializes an instace of 'Exp3MixtureVariableCollection'.
Args:
num_agents: (int) the number of agents mixed by the mixture agent.
reward_aggregat... | Initializes an instace of 'Exp3MixtureVariableCollection'.
Args:
num_agents: (int) the number of agents mixed by the mixture agent.
reward_aggregates: A list of floats containing the reward aggregates for
each agent. If not set, the initial values will be 0.
inverse_temperature: The initi... | __init__ | python | tensorflow/agents | tf_agents/bandits/agents/exp3_mixture_agent.py | https://github.com/tensorflow/agents/blob/master/tf_agents/bandits/agents/exp3_mixture_agent.py | Apache-2.0 |
def __init__(
self,
agents: List[tf_agent.TFAgent],
variable_collection: Optional[Exp3MixtureVariableCollection] = None,
forgetting: float = 0.999,
max_inverse_temperature: float = 1000.0,
name: Optional[Text] = None,
):
"""Initializes an instance of `Exp3MixtureAgent`.
Ar... | Initializes an instance of `Exp3MixtureAgent`.
Args:
agents: List of TF-Agents agents that this mixture agent trains.
variable_collection: An instance of `Exp3VariableCollection`. If not set,
A default one will be created. It contains all the variables that are
needed to restore the mix... | __init__ | python | tensorflow/agents | tf_agents/bandits/agents/exp3_mixture_agent.py | https://github.com/tensorflow/agents/blob/master/tf_agents/bandits/agents/exp3_mixture_agent.py | Apache-2.0 |
def _single_objective_loss(
self,
objective_idx: int,
observations: tf.Tensor,
actions: tf.Tensor,
single_objective_values: tf.Tensor,
weights: types.Tensor = None,
training: bool = False,
) -> tf.Tensor:
"""Computes loss for a single objective.
Args:
objective... | Computes loss for a single objective.
Args:
objective_idx: The index into `self._objective_networks` for a specific
objective network.
observations: A batch of observations.
actions: A batch of actions.
single_objective_values: A batch of objective values shaped as
[batch_si... | _single_objective_loss | python | tensorflow/agents | tf_agents/bandits/agents/greedy_multi_objective_neural_agent.py | https://github.com/tensorflow/agents/blob/master/tf_agents/bandits/agents/greedy_multi_objective_neural_agent.py | Apache-2.0 |
def _loss(
self,
experience: types.NestedTensor,
weights: types.Tensor = None,
training: bool = False,
) -> tf_agent.LossInfo:
"""Computes loss for training the objective networks.
Args:
experience: A batch of experience data in the form of a `Trajectory` or
`Transition`... | Computes loss for training the objective networks.
Args:
experience: A batch of experience data in the form of a `Trajectory` or
`Transition`.
weights: Optional scalar or elementwise (per-batch-entry) importance
weights. The output batch loss will be scaled by these weights, and the
... | _loss | python | tensorflow/agents | tf_agents/bandits/agents/greedy_multi_objective_neural_agent.py | https://github.com/tensorflow/agents/blob/master/tf_agents/bandits/agents/greedy_multi_objective_neural_agent.py | Apache-2.0 |
def __init__(
self,
observation_spec: types.NestedTensorSpec,
kernel_weights: np.ndarray,
bias: np.ndarray,
):
"""A simple linear network.
Args:
observation_spec: The observation specification.
kernel_weights: A 2-d numpy array of shape [input_size, output_size].
bia... | A simple linear network.
Args:
observation_spec: The observation specification.
kernel_weights: A 2-d numpy array of shape [input_size, output_size].
bias: A 1-d numpy array of shape [output_size].
| __init__ | python | tensorflow/agents | tf_agents/bandits/agents/greedy_multi_objective_neural_agent_test.py | https://github.com/tensorflow/agents/blob/master/tf_agents/bandits/agents/greedy_multi_objective_neural_agent_test.py | Apache-2.0 |
def __init__(self, kernel_weights: np.ndarray, bias: np.ndarray):
"""A simple linear heteroscedastic network.
Args:
kernel_weights: A 2-d numpy array of shape [input_size, output_size].
bias: A 1-d numpy array of shape [output_size].
"""
assert len(kernel_weights.shape) == 2
assert len(... | A simple linear heteroscedastic network.
Args:
kernel_weights: A 2-d numpy array of shape [input_size, output_size].
bias: A 1-d numpy array of shape [output_size].
| __init__ | python | tensorflow/agents | tf_agents/bandits/agents/greedy_multi_objective_neural_agent_test.py | https://github.com/tensorflow/agents/blob/master/tf_agents/bandits/agents/greedy_multi_objective_neural_agent_test.py | Apache-2.0 |
def per_example_reward_loss(
self,
observations: types.NestedTensor,
actions: types.Tensor,
rewards: types.Tensor,
weights: Optional[types.Float] = None,
training: bool = False,
) -> types.Tensor:
"""Computes loss for reward prediction training.
Args:
observations: A... | Computes loss for reward prediction training.
Args:
observations: A batch of observations.
actions: A batch of actions.
rewards: A batch of rewards.
weights: Optional scalar or elementwise (per-batch-entry) importance
weights. The output batch loss will be scaled by these weights, ... | per_example_reward_loss | python | tensorflow/agents | tf_agents/bandits/agents/greedy_reward_prediction_agent.py | https://github.com/tensorflow/agents/blob/master/tf_agents/bandits/agents/greedy_reward_prediction_agent.py | Apache-2.0 |
def _loss(
self,
experience: types.NestedTensor,
weights: Optional[types.Float] = None,
training: bool = False,
) -> tf_agent.LossInfo:
"""Computes loss for training the reward and constraint networks.
Args:
experience: A batch of experience data in the form of a `Trajectory` or... | Computes loss for training the reward and constraint networks.
Args:
experience: A batch of experience data in the form of a `Trajectory` or
`Transition`.
weights: Optional scalar or elementwise (per-batch-entry) importance
weights. The output batch loss will be scaled by these weights... | _loss | python | tensorflow/agents | tf_agents/bandits/agents/greedy_reward_prediction_agent.py | https://github.com/tensorflow/agents/blob/master/tf_agents/bandits/agents/greedy_reward_prediction_agent.py | Apache-2.0 |
def __init__(
self,
context_dim: int,
num_models: int,
use_eigendecomp: bool = False,
dtype: tf.DType = tf.float32,
name: Optional[Text] = None,
):
"""Initializes an instance of `LinearBanditVariableCollection`.
It creates all the variables needed for `LinearBanditAgent`.
... | Initializes an instance of `LinearBanditVariableCollection`.
It creates all the variables needed for `LinearBanditAgent`.
Args:
context_dim: (int) The context dimension of the bandit environment the
agent will be used on.
num_models: (int) The number of models maintained by the agent. This... | __init__ | python | tensorflow/agents | tf_agents/bandits/agents/linear_bandit_agent.py | https://github.com/tensorflow/agents/blob/master/tf_agents/bandits/agents/linear_bandit_agent.py | Apache-2.0 |
def update_a_and_b_with_forgetting(
a_prev: types.Tensor,
b_prev: types.Tensor,
r: types.Tensor,
x: types.Tensor,
gamma: float,
) -> Tuple[types.Tensor, types.Tensor]:
r"""Update the covariance matrix `a` and the weighted sum of rewards `b`.
This function updates the covariance matrix `a` and t... | Update the covariance matrix `a` and the weighted sum of rewards `b`.
This function updates the covariance matrix `a` and the sum of weighted
rewards `b` using a forgetting factor `gamma`.
Args:
a_prev: previous estimate of `a`.
b_prev: previous estimate of `b`.
r: a `Tensor` of shape [`batch_size`]... | update_a_and_b_with_forgetting | python | tensorflow/agents | tf_agents/bandits/agents/linear_bandit_agent.py | https://github.com/tensorflow/agents/blob/master/tf_agents/bandits/agents/linear_bandit_agent.py | Apache-2.0 |
def theta(self):
"""Returns the matrix of per-arm feature weights.
The returned matrix has shape (num_actions, context_dim).
It's equivalent to a stacking of theta vectors from the paper.
"""
thetas = []
for k in range(self._num_models):
if self._use_eigendecomp:
model_index = pol... | Returns the matrix of per-arm feature weights.
The returned matrix has shape (num_actions, context_dim).
It's equivalent to a stacking of theta vectors from the paper.
| theta | python | tensorflow/agents | tf_agents/bandits/agents/linear_bandit_agent.py | https://github.com/tensorflow/agents/blob/master/tf_agents/bandits/agents/linear_bandit_agent.py | Apache-2.0 |
def _process_experience(self, experience):
"""Given an experience, returns reward, action, observation, and batch size."""
if self._accepts_per_arm_features:
return self._process_experience_per_arm(experience)
else:
return self._process_experience_global(experience) | Given an experience, returns reward, action, observation, and batch size. | _process_experience | python | tensorflow/agents | tf_agents/bandits/agents/linear_bandit_agent.py | https://github.com/tensorflow/agents/blob/master/tf_agents/bandits/agents/linear_bandit_agent.py | Apache-2.0 |
def _process_experience_per_arm(self, experience):
"""Processes the experience in case the agent accepts per-arm features.
In the experience coming from the replay buffer, the reward (and all other
elements) have two batch dimensions `batch_size` and `time_steps`, where
`time_steps` is the number of dr... | Processes the experience in case the agent accepts per-arm features.
In the experience coming from the replay buffer, the reward (and all other
elements) have two batch dimensions `batch_size` and `time_steps`, where
`time_steps` is the number of driver steps executed in each training loop.
We flatten ... | _process_experience_per_arm | python | tensorflow/agents | tf_agents/bandits/agents/linear_bandit_agent.py | https://github.com/tensorflow/agents/blob/master/tf_agents/bandits/agents/linear_bandit_agent.py | Apache-2.0 |
def _process_experience_global(self, experience):
"""Processes the experience in case the agent accepts only global features.
In the experience coming from the replay buffer, the reward (and all other
elements) have two batch dimensions `batch_size` and `time_steps`, where
`time_steps` is the number of... | Processes the experience in case the agent accepts only global features.
In the experience coming from the replay buffer, the reward (and all other
elements) have two batch dimensions `batch_size` and `time_steps`, where
`time_steps` is the number of driver steps executed in each training loop.
We flat... | _process_experience_global | python | tensorflow/agents | tf_agents/bandits/agents/linear_bandit_agent.py | https://github.com/tensorflow/agents/blob/master/tf_agents/bandits/agents/linear_bandit_agent.py | Apache-2.0 |
def _maybe_apply_per_example_weight(
self,
observation: tf.Tensor,
reward: tf.Tensor,
weights: Optional[tf.Tensor],
) -> Tuple[tf.Tensor, tf.Tensor]:
"""Optionally applies per-example weight to observation and rewards."""
if weights is None:
return (observation, reward)
else:... | Optionally applies per-example weight to observation and rewards. | _maybe_apply_per_example_weight | python | tensorflow/agents | tf_agents/bandits/agents/linear_bandit_agent.py | https://github.com/tensorflow/agents/blob/master/tf_agents/bandits/agents/linear_bandit_agent.py | Apache-2.0 |
def _distributed_train_step(self, experience, weights=None):
"""Distributed train fn to be passed as input to run()."""
experience_reward, action, experience_observation, batch_size = (
self._process_experience(experience)
)
self._train_step_counter.assign_add(batch_size)
observation, reward... | Distributed train fn to be passed as input to run(). | _distributed_train_step | python | tensorflow/agents | tf_agents/bandits/agents/linear_bandit_agent.py | https://github.com/tensorflow/agents/blob/master/tf_agents/bandits/agents/linear_bandit_agent.py | Apache-2.0 |
def _train(self, experience, weights=None):
"""Updates the policy based on the data in `experience`.
Note that `experience` should only contain data points that this agent has
not previously seen. If `experience` comes from a replay buffer, this buffer
should be cleared between each call to `train`.
... | Updates the policy based on the data in `experience`.
Note that `experience` should only contain data points that this agent has
not previously seen. If `experience` comes from a replay buffer, this buffer
should be cleared between each call to `train`.
Args:
experience: A batch of experience da... | _train | python | tensorflow/agents | tf_agents/bandits/agents/linear_bandit_agent.py | https://github.com/tensorflow/agents/blob/master/tf_agents/bandits/agents/linear_bandit_agent.py | Apache-2.0 |
def testLinearAgentUpdate(
self,
batch_size,
context_dim,
exploration_policy,
dtype,
use_eigendecomp=False,
set_example_weights=False,
):
"""Check that the agent updates for specified actions and rewards."""
# Construct a `Trajectory` for the given action, observatio... | Check that the agent updates for specified actions and rewards. | testLinearAgentUpdate | python | tensorflow/agents | tf_agents/bandits/agents/linear_bandit_agent_test.py | https://github.com/tensorflow/agents/blob/master/tf_agents/bandits/agents/linear_bandit_agent_test.py | Apache-2.0 |
def testDistributedLinearAgentUpdate(
self,
batch_size,
context_dim,
exploration_policy,
dtype,
use_eigendecomp=False,
set_example_weights=False,
):
"""Same as above, but uses the distributed train function of the agent."""
del use_eigendecomp # Unused in this test.
... | Same as above, but uses the distributed train function of the agent. | testDistributedLinearAgentUpdate | python | tensorflow/agents | tf_agents/bandits/agents/linear_bandit_agent_test.py | https://github.com/tensorflow/agents/blob/master/tf_agents/bandits/agents/linear_bandit_agent_test.py | Apache-2.0 |
def _dynamic_partition_of_nested_tensors(
nested_tensor: types.NestedTensor,
partitions: types.Int,
num_partitions: int,
) -> List[types.NestedTensor]:
"""This function takes a nested structure and partitions every element of it.
Specifically it outputs a list of nest that all have the same structure a... | This function takes a nested structure and partitions every element of it.
Specifically it outputs a list of nest that all have the same structure as the
original, and every element of the list is a nest that contains a dynamic
partition of the corresponding original tensors.
Note that this function uses tf.d... | _dynamic_partition_of_nested_tensors | python | tensorflow/agents | tf_agents/bandits/agents/mixture_agent.py | https://github.com/tensorflow/agents/blob/master/tf_agents/bandits/agents/mixture_agent.py | Apache-2.0 |
def __init__(
self,
mixture_distribution: types.Distribution,
agents: Sequence[tf_agent.TFAgent],
name: Optional[Text] = None,
):
"""Initializes an instance of `MixtureAgent`.
Args:
mixture_distribution: An instance of `tfd.Categorical` distribution. This
distribution is... | Initializes an instance of `MixtureAgent`.
Args:
mixture_distribution: An instance of `tfd.Categorical` distribution. This
distribution is used to draw sub-policies by the mixture policy. The
parameters of the distribution is trained by the mixture agent.
agents: List of instances of TF... | __init__ | python | tensorflow/agents | tf_agents/bandits/agents/mixture_agent.py | https://github.com/tensorflow/agents/blob/master/tf_agents/bandits/agents/mixture_agent.py | Apache-2.0 |
def _update_mixture_distribution(self, experience):
"""This function updates the mixture weights given training experience."""
raise NotImplementedError(
'`_update_mixture_distribution` should be '
'implemented by subclasses of `MixtureAgent`.'
) | This function updates the mixture weights given training experience. | _update_mixture_distribution | python | tensorflow/agents | tf_agents/bandits/agents/mixture_agent.py | https://github.com/tensorflow/agents/blob/master/tf_agents/bandits/agents/mixture_agent.py | Apache-2.0 |
def __init__(
self,
num_actions: int,
encoding_dim: int,
dtype: tf.DType = tf.float64,
name: Optional[Text] = None,
):
"""Initializes an instance of `NeuralLinUCBVariableCollection`.
Args:
num_actions: (int) number of actions the agent acts on.
encoding_dim: (int) Th... | Initializes an instance of `NeuralLinUCBVariableCollection`.
Args:
num_actions: (int) number of actions the agent acts on.
encoding_dim: (int) The dimensionality of the output of the encoding
network.
dtype: The type of the variables. Should be one of `tf.float32` and
`tf.float64`... | __init__ | python | tensorflow/agents | tf_agents/bandits/agents/neural_linucb_agent.py | https://github.com/tensorflow/agents/blob/master/tf_agents/bandits/agents/neural_linucb_agent.py | Apache-2.0 |
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