code stringlengths 66 870k | docstring stringlengths 19 26.7k | func_name stringlengths 1 138 | language stringclasses 1
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def _compute_kl_constraint(self, all_samples, all_params, set_grad=True):
"""Compute KL divergence.
For each task, compute the KL divergence between the old policy
distribution and current policy distribution.
Args:
all_samples (list[list[_MAMLEpisodeBatch]]): Two
... | Compute KL divergence.
For each task, compute the KL divergence between the old policy
distribution and current policy distribution.
Args:
all_samples (list[list[_MAMLEpisodeBatch]]): Two
dimensional list of _MAMLEpisodeBatch of size
[meta_batch_size... | _compute_kl_constraint | python | rlworkgroup/garage | src/garage/torch/algos/maml.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/torch/algos/maml.py | MIT |
def _compute_policy_entropy(self, task_samples):
"""Compute policy entropy.
Args:
task_samples (list[_MAMLEpisodeBatch]): Samples data for
one task.
Returns:
torch.Tensor: Computed entropy value.
"""
obs = torch.cat([samples.observations... | Compute policy entropy.
Args:
task_samples (list[_MAMLEpisodeBatch]): Samples data for
one task.
Returns:
torch.Tensor: Computed entropy value.
| _compute_policy_entropy | python | rlworkgroup/garage | src/garage/torch/algos/maml.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/torch/algos/maml.py | MIT |
def _process_samples(self, episodes):
"""Process sample data based on the collected paths.
Args:
episodes (EpisodeBatch): Collected batch of episodes.
Returns:
_MAMLEpisodeBatch: Processed samples data.
"""
paths = episodes.to_list()
for path in... | Process sample data based on the collected paths.
Args:
episodes (EpisodeBatch): Collected batch of episodes.
Returns:
_MAMLEpisodeBatch: Processed samples data.
| _process_samples | python | rlworkgroup/garage | src/garage/torch/algos/maml.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/torch/algos/maml.py | MIT |
def _log_performance(self, itr, all_samples, loss_before, loss_after,
kl_before, kl, policy_entropy):
"""Evaluate performance of this batch.
Args:
itr (int): Iteration number.
all_samples (list[list[_MAMLEpisodeBatch]]): Two
dimensional l... | Evaluate performance of this batch.
Args:
itr (int): Iteration number.
all_samples (list[list[_MAMLEpisodeBatch]]): Two
dimensional list of _MAMLEpisodeBatch of size
[meta_batch_size * (num_grad_updates + 1)]
loss_before (float): Loss before o... | _log_performance | python | rlworkgroup/garage | src/garage/torch/algos/maml.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/torch/algos/maml.py | MIT |
def adapt_policy(self, exploration_policy, exploration_episodes):
"""Adapt the policy by one gradient steps for a task.
Args:
exploration_policy (Policy): A policy which was returned from
get_exploration_policy(), and which generated
exploration_episodes by i... | Adapt the policy by one gradient steps for a task.
Args:
exploration_policy (Policy): A policy which was returned from
get_exploration_policy(), and which generated
exploration_episodes by interacting with an environment.
The caller may not use this o... | adapt_policy | python | rlworkgroup/garage | src/garage/torch/algos/maml.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/torch/algos/maml.py | MIT |
def _get_log_alpha(self, samples_data):
"""Return the value of log_alpha.
Args:
samples_data (dict): Transitions(S,A,R,S') that are sampled from
the replay buffer. It should have the keys 'observation',
'action', 'reward', 'terminal', and 'next_observations'.... | Return the value of log_alpha.
Args:
samples_data (dict): Transitions(S,A,R,S') that are sampled from
the replay buffer. It should have the keys 'observation',
'action', 'reward', 'terminal', and 'next_observations'.
Note:
samples_data's entries ... | _get_log_alpha | python | rlworkgroup/garage | src/garage/torch/algos/mtsac.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/torch/algos/mtsac.py | MIT |
def _evaluate_policy(self, epoch):
"""Evaluate the performance of the policy via deterministic sampling.
Statistics such as (average) discounted return and success rate are
recorded.
Args:
epoch (int): The current training epoch.
Returns:
float:... | Evaluate the performance of the policy via deterministic sampling.
Statistics such as (average) discounted return and success rate are
recorded.
Args:
epoch (int): The current training epoch.
Returns:
float: The average return across self._num_evaluatio... | _evaluate_policy | python | rlworkgroup/garage | src/garage/torch/algos/mtsac.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/torch/algos/mtsac.py | MIT |
def to(self, device=None):
"""Put all the networks within the model on device.
Args:
device (str): ID of GPU or CPU.
"""
super().to(device)
if device is None:
device = global_device()
if not self._use_automatic_entropy_tuning:
self._l... | Put all the networks within the model on device.
Args:
device (str): ID of GPU or CPU.
| to | python | rlworkgroup/garage | src/garage/torch/algos/mtsac.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/torch/algos/mtsac.py | MIT |
def __getstate__(self):
"""Object.__getstate__.
Returns:
dict: the state to be pickled for the instance.
"""
data = self.__dict__.copy()
del data['_replay_buffers']
del data['_context_replay_buffers']
return data | Object.__getstate__.
Returns:
dict: the state to be pickled for the instance.
| __getstate__ | python | rlworkgroup/garage | src/garage/torch/algos/pearl.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/torch/algos/pearl.py | MIT |
def __setstate__(self, state):
"""Object.__setstate__.
Args:
state (dict): unpickled state.
"""
self.__dict__.update(state)
self._replay_buffers = {
i: PathBuffer(self._replay_buffer_size)
for i in range(self._num_train_tasks)
}
... | Object.__setstate__.
Args:
state (dict): unpickled state.
| __setstate__ | python | rlworkgroup/garage | src/garage/torch/algos/pearl.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/torch/algos/pearl.py | MIT |
def train(self, trainer):
"""Obtain samples, train, and evaluate for each epoch.
Args:
trainer (Trainer): Gives the algorithm the access to
:method:`Trainer..step_epochs()`, which provides services
such as snapshotting and sampler control.
"""
... | Obtain samples, train, and evaluate for each epoch.
Args:
trainer (Trainer): Gives the algorithm the access to
:method:`Trainer..step_epochs()`, which provides services
such as snapshotting and sampler control.
| train | python | rlworkgroup/garage | src/garage/torch/algos/pearl.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/torch/algos/pearl.py | MIT |
def _optimize_policy(self, indices):
"""Perform algorithm optimizing.
Args:
indices (list): Tasks used for training.
"""
num_tasks = len(indices)
context = self._sample_context(indices)
# clear context and reset belief of policy
self._policy.reset_be... | Perform algorithm optimizing.
Args:
indices (list): Tasks used for training.
| _optimize_policy | python | rlworkgroup/garage | src/garage/torch/algos/pearl.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/torch/algos/pearl.py | MIT |
def _obtain_samples(self,
trainer,
itr,
num_samples,
update_posterior_rate,
add_to_enc_buffer=True):
"""Obtain samples.
Args:
trainer (Trainer): Trainer.
itr (... | Obtain samples.
Args:
trainer (Trainer): Trainer.
itr (int): Index of iteration (epoch).
num_samples (int): Number of samples to obtain.
update_posterior_rate (int): How often (in episodes) to infer
posterior of policy.
add_to_enc_buff... | _obtain_samples | python | rlworkgroup/garage | src/garage/torch/algos/pearl.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/torch/algos/pearl.py | MIT |
def _sample_data(self, indices):
"""Sample batch of training data from a list of tasks.
Args:
indices (list): List of task indices to sample from.
Returns:
torch.Tensor: Obervations, with shape :math:`(X, N, O^*)` where X
is the number of tasks. N is bat... | Sample batch of training data from a list of tasks.
Args:
indices (list): List of task indices to sample from.
Returns:
torch.Tensor: Obervations, with shape :math:`(X, N, O^*)` where X
is the number of tasks. N is batch size.
torch.Tensor: Actions, ... | _sample_data | python | rlworkgroup/garage | src/garage/torch/algos/pearl.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/torch/algos/pearl.py | MIT |
def _sample_context(self, indices):
"""Sample batch of context from a list of tasks.
Args:
indices (list): List of task indices to sample from.
Returns:
torch.Tensor: Context data, with shape :math:`(X, N, C)`. X is the
number of tasks. N is batch size. ... | Sample batch of context from a list of tasks.
Args:
indices (list): List of task indices to sample from.
Returns:
torch.Tensor: Context data, with shape :math:`(X, N, C)`. X is the
number of tasks. N is batch size. C is the combined size of
obser... | _sample_context | python | rlworkgroup/garage | src/garage/torch/algos/pearl.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/torch/algos/pearl.py | MIT |
def _update_target_network(self):
"""Update parameters in the target vf network."""
for target_param, param in zip(self.target_vf.parameters(),
self._vf.parameters()):
target_param.data.copy_(target_param.data *
(1.0 ... | Update parameters in the target vf network. | _update_target_network | python | rlworkgroup/garage | src/garage/torch/algos/pearl.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/torch/algos/pearl.py | MIT |
def networks(self):
"""Return all the networks within the model.
Returns:
list: A list of networks.
"""
return self._policy.networks + [self._policy] + [
self._qf1, self._qf2, self._vf, self.target_vf
] | Return all the networks within the model.
Returns:
list: A list of networks.
| networks | python | rlworkgroup/garage | src/garage/torch/algos/pearl.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/torch/algos/pearl.py | MIT |
def adapt_policy(self, exploration_policy, exploration_episodes):
"""Produce a policy adapted for a task.
Args:
exploration_policy (Policy): A policy which was returned from
get_exploration_policy(), and which generated
exploration_episodes by interacting wit... | Produce a policy adapted for a task.
Args:
exploration_policy (Policy): A policy which was returned from
get_exploration_policy(), and which generated
exploration_episodes by interacting with an environment.
The caller may not use this object after pa... | adapt_policy | python | rlworkgroup/garage | src/garage/torch/algos/pearl.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/torch/algos/pearl.py | MIT |
def to(self, device=None):
"""Put all the networks within the model on device.
Args:
device (str): ID of GPU or CPU.
"""
device = device or global_device()
for net in self.networks:
net.to(device) | Put all the networks within the model on device.
Args:
device (str): ID of GPU or CPU.
| to | python | rlworkgroup/garage | src/garage/torch/algos/pearl.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/torch/algos/pearl.py | MIT |
def augment_env_spec(cls, env_spec, latent_dim):
"""Augment environment by a size of latent dimension.
Args:
env_spec (EnvSpec): Environment specs to be augmented.
latent_dim (int): Latent dimension.
Returns:
EnvSpec: Augmented environment specs.
""... | Augment environment by a size of latent dimension.
Args:
env_spec (EnvSpec): Environment specs to be augmented.
latent_dim (int): Latent dimension.
Returns:
EnvSpec: Augmented environment specs.
| augment_env_spec | python | rlworkgroup/garage | src/garage/torch/algos/pearl.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/torch/algos/pearl.py | MIT |
def get_env_spec(cls, env_spec, latent_dim, module):
"""Get environment specs of encoder with latent dimension.
Args:
env_spec (EnvSpec): Environment specification.
latent_dim (int): Latent dimension.
module (str): Module to get environment specs for.
Return... | Get environment specs of encoder with latent dimension.
Args:
env_spec (EnvSpec): Environment specification.
latent_dim (int): Latent dimension.
module (str): Module to get environment specs for.
Returns:
InOutSpec: Module environment specs with latent d... | get_env_spec | python | rlworkgroup/garage | src/garage/torch/algos/pearl.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/torch/algos/pearl.py | MIT |
def step_episode(self):
"""Take a single time-step in the current episode.
Returns:
bool: True iff the episode is done, either due to the environment
indicating termination of due to reaching `max_episode_length`.
"""
if self._eps_length < self._max_episode_leng... | Take a single time-step in the current episode.
Returns:
bool: True iff the episode is done, either due to the environment
indicating termination of due to reaching `max_episode_length`.
| step_episode | python | rlworkgroup/garage | src/garage/torch/algos/pearl.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/torch/algos/pearl.py | MIT |
def rollout(self):
"""Sample a single episode of the agent in the environment.
Returns:
EpisodeBatch: The collected episode.
"""
self.agent.sample_from_belief()
self.start_episode()
while not self.step_episode():
pass
return self.collect_... | Sample a single episode of the agent in the environment.
Returns:
EpisodeBatch: The collected episode.
| rollout | python | rlworkgroup/garage | src/garage/torch/algos/pearl.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/torch/algos/pearl.py | MIT |
def _compute_objective(self, advantages, obs, actions, rewards):
r"""Compute objective value.
Args:
advantages (torch.Tensor): Advantage value at each step
with shape :math:`(N \dot [T], )`.
obs (torch.Tensor): Observation from the environment
wit... | Compute objective value.
Args:
advantages (torch.Tensor): Advantage value at each step
with shape :math:`(N \dot [T], )`.
obs (torch.Tensor): Observation from the environment
with shape :math:`(N \dot [T], O*)`.
actions (torch.Tensor): Actions... | _compute_objective | python | rlworkgroup/garage | src/garage/torch/algos/ppo.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/torch/algos/ppo.py | MIT |
def train(self, trainer):
"""Obtain samplers and start actual training for each epoch.
Args:
trainer (Trainer): Gives the algorithm the access to
:method:`~Trainer.step_epochs()`, which provides services
such as snapshotting and sampler control.
Retu... | Obtain samplers and start actual training for each epoch.
Args:
trainer (Trainer): Gives the algorithm the access to
:method:`~Trainer.step_epochs()`, which provides services
such as snapshotting and sampler control.
Returns:
float: The average r... | train | python | rlworkgroup/garage | src/garage/torch/algos/sac.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/torch/algos/sac.py | MIT |
def train_once(self, itr=None, paths=None):
"""Complete 1 training iteration of SAC.
Args:
itr (int): Iteration number. This argument is deprecated.
paths (list[dict]): A list of collected paths.
This argument is deprecated.
Returns:
torch.Te... | Complete 1 training iteration of SAC.
Args:
itr (int): Iteration number. This argument is deprecated.
paths (list[dict]): A list of collected paths.
This argument is deprecated.
Returns:
torch.Tensor: loss from actor/policy network after optimization... | train_once | python | rlworkgroup/garage | src/garage/torch/algos/sac.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/torch/algos/sac.py | MIT |
def _get_log_alpha(self, samples_data):
"""Return the value of log_alpha.
Args:
samples_data (dict): Transitions(S,A,R,S') that are sampled from
the replay buffer. It should have the keys 'observation',
'action', 'reward', 'terminal', and 'next_observations'.... | Return the value of log_alpha.
Args:
samples_data (dict): Transitions(S,A,R,S') that are sampled from
the replay buffer. It should have the keys 'observation',
'action', 'reward', 'terminal', and 'next_observations'.
This function exists in case there are ve... | _get_log_alpha | python | rlworkgroup/garage | src/garage/torch/algos/sac.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/torch/algos/sac.py | MIT |
def _temperature_objective(self, log_pi, samples_data):
"""Compute the temperature/alpha coefficient loss.
Args:
log_pi(torch.Tensor): log probability of actions that are sampled
from the replay buffer. Shape is (1, buffer_batch_size).
samples_data (dict): Transi... | Compute the temperature/alpha coefficient loss.
Args:
log_pi(torch.Tensor): log probability of actions that are sampled
from the replay buffer. Shape is (1, buffer_batch_size).
samples_data (dict): Transitions(S,A,R,S') that are sampled from
the replay bu... | _temperature_objective | python | rlworkgroup/garage | src/garage/torch/algos/sac.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/torch/algos/sac.py | MIT |
def _actor_objective(self, samples_data, new_actions, log_pi_new_actions):
"""Compute the Policy/Actor loss.
Args:
samples_data (dict): Transitions(S,A,R,S') that are sampled from
the replay buffer. It should have the keys 'observation',
'action', 'reward', '... | Compute the Policy/Actor loss.
Args:
samples_data (dict): Transitions(S,A,R,S') that are sampled from
the replay buffer. It should have the keys 'observation',
'action', 'reward', 'terminal', and 'next_observations'.
new_actions (torch.Tensor): Actions re... | _actor_objective | python | rlworkgroup/garage | src/garage/torch/algos/sac.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/torch/algos/sac.py | MIT |
def _critic_objective(self, samples_data):
"""Compute the Q-function/critic loss.
Args:
samples_data (dict): Transitions(S,A,R,S') that are sampled from
the replay buffer. It should have the keys 'observation',
'action', 'reward', 'terminal', and 'next_observ... | Compute the Q-function/critic loss.
Args:
samples_data (dict): Transitions(S,A,R,S') that are sampled from
the replay buffer. It should have the keys 'observation',
'action', 'reward', 'terminal', and 'next_observations'.
Note:
samples_data's ent... | _critic_objective | python | rlworkgroup/garage | src/garage/torch/algos/sac.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/torch/algos/sac.py | MIT |
def _caps_regularization_objective(self, action_dists, samples_data):
"""Compute the spatial and temporal regularization loss as in CAPS.
Args:
samples_data (dict): Transitions(S,A,R,S') that are sampled from
the replay buffer. It should have the keys 'observation',
... | Compute the spatial and temporal regularization loss as in CAPS.
Args:
samples_data (dict): Transitions(S,A,R,S') that are sampled from
the replay buffer. It should have the keys 'observation',
'action', 'reward', 'terminal', and 'next_observations'.
acti... | _caps_regularization_objective | python | rlworkgroup/garage | src/garage/torch/algos/sac.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/torch/algos/sac.py | MIT |
def _update_targets(self):
"""Update parameters in the target q-functions."""
target_qfs = [self._target_qf1, self._target_qf2]
qfs = [self._qf1, self._qf2]
for target_qf, qf in zip(target_qfs, qfs):
for t_param, param in zip(target_qf.parameters(), qf.parameters()):
... | Update parameters in the target q-functions. | _update_targets | python | rlworkgroup/garage | src/garage/torch/algos/sac.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/torch/algos/sac.py | MIT |
def optimize_policy(self, samples_data):
"""Optimize the policy q_functions, and temperature coefficient.
Args:
samples_data (dict): Transitions(S,A,R,S') that are sampled from
the replay buffer. It should have the keys 'observation',
'action', 'reward', 'ter... | Optimize the policy q_functions, and temperature coefficient.
Args:
samples_data (dict): Transitions(S,A,R,S') that are sampled from
the replay buffer. It should have the keys 'observation',
'action', 'reward', 'terminal', and 'next_observations'.
Note:
... | optimize_policy | python | rlworkgroup/garage | src/garage/torch/algos/sac.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/torch/algos/sac.py | MIT |
def _evaluate_policy(self, epoch):
"""Evaluate the performance of the policy via deterministic sampling.
Statistics such as (average) discounted return and success rate are
recorded.
Args:
epoch (int): The current training epoch.
Returns:
float:... | Evaluate the performance of the policy via deterministic sampling.
Statistics such as (average) discounted return and success rate are
recorded.
Args:
epoch (int): The current training epoch.
Returns:
float: The average return across self._num_evaluatio... | _evaluate_policy | python | rlworkgroup/garage | src/garage/torch/algos/sac.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/torch/algos/sac.py | MIT |
def _log_statistics(self, policy_loss, qf1_loss, qf2_loss):
"""Record training statistics to dowel such as losses and returns.
Args:
policy_loss (torch.Tensor): loss from actor/policy network.
qf1_loss (torch.Tensor): loss from 1st qf/critic network.
qf2_loss (torch.... | Record training statistics to dowel such as losses and returns.
Args:
policy_loss (torch.Tensor): loss from actor/policy network.
qf1_loss (torch.Tensor): loss from 1st qf/critic network.
qf2_loss (torch.Tensor): loss from 2nd qf/critic network.
| _log_statistics | python | rlworkgroup/garage | src/garage/torch/algos/sac.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/torch/algos/sac.py | MIT |
def networks(self):
"""Return all the networks within the model.
Returns:
list: A list of networks.
"""
return [
self.policy, self._qf1, self._qf2, self._target_qf1,
self._target_qf2
] | Return all the networks within the model.
Returns:
list: A list of networks.
| networks | python | rlworkgroup/garage | src/garage/torch/algos/sac.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/torch/algos/sac.py | MIT |
def to(self, device=None):
"""Put all the networks within the model on device.
Args:
device (str): ID of GPU or CPU.
"""
if device is None:
device = global_device()
for net in self.networks:
net.to(device)
if not self._use_automatic_e... | Put all the networks within the model on device.
Args:
device (str): ID of GPU or CPU.
| to | python | rlworkgroup/garage | src/garage/torch/algos/sac.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/torch/algos/sac.py | MIT |
def _get_action(self, action, noise_scale):
"""Select action based on policy.
Action can be added with noise.
Args:
action (float): Action.
noise_scale (float): Noise scale added to action.
Return:
float: Action selected by the policy.
"""
... | Select action based on policy.
Action can be added with noise.
Args:
action (float): Action.
noise_scale (float): Noise scale added to action.
Return:
float: Action selected by the policy.
| _get_action | python | rlworkgroup/garage | src/garage/torch/algos/td3.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/torch/algos/td3.py | MIT |
def train(self, trainer):
"""Obtain samplers and start actual training for each epoch.
Args:
trainer (Trainer): Experiment trainer, which provides services
such as snapshotting and sampler control.
"""
if not self._eval_env:
self._eval_env = trai... | Obtain samplers and start actual training for each epoch.
Args:
trainer (Trainer): Experiment trainer, which provides services
such as snapshotting and sampler control.
| train | python | rlworkgroup/garage | src/garage/torch/algos/td3.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/torch/algos/td3.py | MIT |
def _train_once(self, itr):
"""Perform one iteration of training.
Args:
itr (int): Iteration number.
"""
for grad_step_timer in range(self._grad_steps_per_env_step):
if (self._replay_buffer.n_transitions_stored >=
self._min_buffer_size):
... | Perform one iteration of training.
Args:
itr (int): Iteration number.
| _train_once | python | rlworkgroup/garage | src/garage/torch/algos/td3.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/torch/algos/td3.py | MIT |
def _optimize_policy(self, samples_data, grad_step_timer):
"""Perform algorithm optimization.
Args:
samples_data (dict): Processed batch data.
grad_step_timer (int): Iteration number of the gradient time
taken in the env.
Returns:
float: Loss... | Perform algorithm optimization.
Args:
samples_data (dict): Processed batch data.
grad_step_timer (int): Iteration number of the gradient time
taken in the env.
Returns:
float: Loss predicted by the q networks
(critic networks).
... | _optimize_policy | python | rlworkgroup/garage | src/garage/torch/algos/td3.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/torch/algos/td3.py | MIT |
def _evaluate_policy(self):
"""Evaluate the performance of the policy via deterministic rollouts.
Statistics such as (average) discounted return and success rate are
recorded.
Returns:
TrajectoryBatch: Evaluation trajectories, representing the best
curre... | Evaluate the performance of the policy via deterministic rollouts.
Statistics such as (average) discounted return and success rate are
recorded.
Returns:
TrajectoryBatch: Evaluation trajectories, representing the best
current performance of the algorithm.
... | _evaluate_policy | python | rlworkgroup/garage | src/garage/torch/algos/td3.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/torch/algos/td3.py | MIT |
def _update_network_parameters(self):
"""Update parameters in actor network and critic networks."""
soft_update_model(self._target_qf_1, self._qf_1, self._tau)
soft_update_model(self._target_qf_2, self._qf_2, self._tau)
soft_update_model(self._target_policy, self.policy, self._tau) | Update parameters in actor network and critic networks. | _update_network_parameters | python | rlworkgroup/garage | src/garage/torch/algos/td3.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/torch/algos/td3.py | MIT |
def _log_statistics(self):
"""Output training statistics to dowel such as losses and returns."""
tabular.record('Policy/AveragePolicyLoss',
np.mean(self._episode_policy_losses))
tabular.record('QFunction/AverageQFunctionLoss',
np.mean(self._episode_q... | Output training statistics to dowel such as losses and returns. | _log_statistics | python | rlworkgroup/garage | src/garage/torch/algos/td3.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/torch/algos/td3.py | MIT |
def networks(self):
"""Return all the networks within the model.
Returns:
list: A list of networks.
"""
return [
self.policy, self._qf_1, self._qf_2, self._target_policy,
self._target_qf_1, self._target_qf_2
] | Return all the networks within the model.
Returns:
list: A list of networks.
| networks | python | rlworkgroup/garage | src/garage/torch/algos/td3.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/torch/algos/td3.py | MIT |
def to(self, device=None):
"""Put all the networks within the model on device.
Args:
device (str): ID of GPU or CPU.
"""
device = device or global_device()
for net in self.networks:
net.to(device) | Put all the networks within the model on device.
Args:
device (str): ID of GPU or CPU.
| to | python | rlworkgroup/garage | src/garage/torch/algos/td3.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/torch/algos/td3.py | MIT |
def _compute_objective(self, advantages, obs, actions, rewards):
r"""Compute objective value.
Args:
advantages (torch.Tensor): Advantage value at each step
with shape :math:`(N \dot [T], )`.
obs (torch.Tensor): Observation from the environment
wit... | Compute objective value.
Args:
advantages (torch.Tensor): Advantage value at each step
with shape :math:`(N \dot [T], )`.
obs (torch.Tensor): Observation from the environment
with shape :math:`(N \dot [T], O*)`.
actions (torch.Tensor): Actions... | _compute_objective | python | rlworkgroup/garage | src/garage/torch/algos/trpo.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/torch/algos/trpo.py | MIT |
def _train_policy(self, obs, actions, rewards, advantages):
r"""Train the policy.
Args:
obs (torch.Tensor): Observation from the environment
with shape :math:`(N, O*)`.
actions (torch.Tensor): Actions fed to the environment
with shape :math:`(N, A... | Train the policy.
Args:
obs (torch.Tensor): Observation from the environment
with shape :math:`(N, O*)`.
actions (torch.Tensor): Actions fed to the environment
with shape :math:`(N, A*)`.
rewards (torch.Tensor): Acquired rewards
... | _train_policy | python | rlworkgroup/garage | src/garage/torch/algos/trpo.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/torch/algos/trpo.py | MIT |
def _train_once(self, itr, eps):
"""Train the algorithm once.
Args:
itr (int): Iteration number.
eps (EpisodeBatch): A batch of collected paths.
Returns:
numpy.float64: Calculated mean value of undiscounted returns.
"""
obs = np_to_torch(eps... | Train the algorithm once.
Args:
itr (int): Iteration number.
eps (EpisodeBatch): A batch of collected paths.
Returns:
numpy.float64: Calculated mean value of undiscounted returns.
| _train_once | python | rlworkgroup/garage | src/garage/torch/algos/vpg.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/torch/algos/vpg.py | MIT |
def train(self, trainer):
"""Obtain samplers and start actual training for each epoch.
Args:
trainer (Trainer): Gives the algorithm the access to
:method:`~Trainer.step_epochs()`, which provides services
such as snapshotting and sampler control.
Retu... | Obtain samplers and start actual training for each epoch.
Args:
trainer (Trainer): Gives the algorithm the access to
:method:`~Trainer.step_epochs()`, which provides services
such as snapshotting and sampler control.
Returns:
float: The average r... | train | python | rlworkgroup/garage | src/garage/torch/algos/vpg.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/torch/algos/vpg.py | MIT |
def _train(self, obs, actions, rewards, returns, advs):
r"""Train the policy and value function with minibatch.
Args:
obs (torch.Tensor): Observation from the environment with shape
:math:`(N, O*)`.
actions (torch.Tensor): Actions fed to the environment with shap... | Train the policy and value function with minibatch.
Args:
obs (torch.Tensor): Observation from the environment with shape
:math:`(N, O*)`.
actions (torch.Tensor): Actions fed to the environment with shape
:math:`(N, A*)`.
rewards (torch.Tensor... | _train | python | rlworkgroup/garage | src/garage/torch/algos/vpg.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/torch/algos/vpg.py | MIT |
def _train_policy(self, obs, actions, rewards, advantages):
r"""Train the policy.
Args:
obs (torch.Tensor): Observation from the environment
with shape :math:`(N, O*)`.
actions (torch.Tensor): Actions fed to the environment
with shape :math:`(N, A... | Train the policy.
Args:
obs (torch.Tensor): Observation from the environment
with shape :math:`(N, O*)`.
actions (torch.Tensor): Actions fed to the environment
with shape :math:`(N, A*)`.
rewards (torch.Tensor): Acquired rewards
... | _train_policy | python | rlworkgroup/garage | src/garage/torch/algos/vpg.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/torch/algos/vpg.py | MIT |
def _train_value_function(self, obs, returns):
r"""Train the value function.
Args:
obs (torch.Tensor): Observation from the environment
with shape :math:`(N, O*)`.
returns (torch.Tensor): Acquired returns
with shape :math:`(N, )`.
Returns... | Train the value function.
Args:
obs (torch.Tensor): Observation from the environment
with shape :math:`(N, O*)`.
returns (torch.Tensor): Acquired returns
with shape :math:`(N, )`.
Returns:
torch.Tensor: Calculated mean scalar value of... | _train_value_function | python | rlworkgroup/garage | src/garage/torch/algos/vpg.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/torch/algos/vpg.py | MIT |
def _compute_loss(self, obs, actions, rewards, valids, baselines):
r"""Compute mean value of loss.
Notes: P is the maximum episode length (self.max_episode_length)
Args:
obs (torch.Tensor): Observation from the environment
with shape :math:`(N, P, O*)`.
... | Compute mean value of loss.
Notes: P is the maximum episode length (self.max_episode_length)
Args:
obs (torch.Tensor): Observation from the environment
with shape :math:`(N, P, O*)`.
actions (torch.Tensor): Actions fed to the environment
with sha... | _compute_loss | python | rlworkgroup/garage | src/garage/torch/algos/vpg.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/torch/algos/vpg.py | MIT |
def _compute_loss_with_adv(self, obs, actions, rewards, advantages):
r"""Compute mean value of loss.
Args:
obs (torch.Tensor): Observation from the environment
with shape :math:`(N \dot [T], O*)`.
actions (torch.Tensor): Actions fed to the environment
... | Compute mean value of loss.
Args:
obs (torch.Tensor): Observation from the environment
with shape :math:`(N \dot [T], O*)`.
actions (torch.Tensor): Actions fed to the environment
with shape :math:`(N \dot [T], A*)`.
rewards (torch.Tensor): Acq... | _compute_loss_with_adv | python | rlworkgroup/garage | src/garage/torch/algos/vpg.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/torch/algos/vpg.py | MIT |
def _compute_advantage(self, rewards, valids, baselines):
r"""Compute mean value of loss.
Notes: P is the maximum episode length (self.max_episode_length)
Args:
rewards (torch.Tensor): Acquired rewards
with shape :math:`(N, P)`.
valids (list[int]): Numbe... | Compute mean value of loss.
Notes: P is the maximum episode length (self.max_episode_length)
Args:
rewards (torch.Tensor): Acquired rewards
with shape :math:`(N, P)`.
valids (list[int]): Numbers of valid steps in each episode
baselines (torch.Tensor)... | _compute_advantage | python | rlworkgroup/garage | src/garage/torch/algos/vpg.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/torch/algos/vpg.py | MIT |
def _compute_kl_constraint(self, obs):
r"""Compute KL divergence.
Compute the KL divergence between the old policy distribution and
current policy distribution.
Notes: P is the maximum episode length (self.max_episode_length)
Args:
obs (torch.Tensor): Observation f... | Compute KL divergence.
Compute the KL divergence between the old policy distribution and
current policy distribution.
Notes: P is the maximum episode length (self.max_episode_length)
Args:
obs (torch.Tensor): Observation from the environment
with shape :mat... | _compute_kl_constraint | python | rlworkgroup/garage | src/garage/torch/algos/vpg.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/torch/algos/vpg.py | MIT |
def _compute_policy_entropy(self, obs):
r"""Compute entropy value of probability distribution.
Notes: P is the maximum episode length (self.max_episode_length)
Args:
obs (torch.Tensor): Observation from the environment
with shape :math:`(N, P, O*)`.
Returns... | Compute entropy value of probability distribution.
Notes: P is the maximum episode length (self.max_episode_length)
Args:
obs (torch.Tensor): Observation from the environment
with shape :math:`(N, P, O*)`.
Returns:
torch.Tensor: Calculated entropy value... | _compute_policy_entropy | python | rlworkgroup/garage | src/garage/torch/algos/vpg.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/torch/algos/vpg.py | MIT |
def _compute_objective(self, advantages, obs, actions, rewards):
r"""Compute objective value.
Args:
advantages (torch.Tensor): Advantage value at each step
with shape :math:`(N \dot [T], )`.
obs (torch.Tensor): Observation from the environment
wit... | Compute objective value.
Args:
advantages (torch.Tensor): Advantage value at each step
with shape :math:`(N \dot [T], )`.
obs (torch.Tensor): Observation from the environment
with shape :math:`(N \dot [T], O*)`.
actions (torch.Tensor): Actions... | _compute_objective | python | rlworkgroup/garage | src/garage/torch/algos/vpg.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/torch/algos/vpg.py | MIT |
def log_prob(self, value, pre_tanh_value=None, epsilon=1e-6):
"""The log likelihood of a sample on the this Tanh Distribution.
Args:
value (torch.Tensor): The sample whose loglikelihood is being
computed.
pre_tanh_value (torch.Tensor): The value prior to having t... | The log likelihood of a sample on the this Tanh Distribution.
Args:
value (torch.Tensor): The sample whose loglikelihood is being
computed.
pre_tanh_value (torch.Tensor): The value prior to having the tanh
function applied to it but after it has been samp... | log_prob | python | rlworkgroup/garage | src/garage/torch/distributions/tanh_normal.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/torch/distributions/tanh_normal.py | MIT |
def rsample_with_pre_tanh_value(self, sample_shape=torch.Size()):
"""Return a sample, sampled from this TanhNormal distribution.
Returns the sampled value before the tanh transform is applied and the
sampled value with the tanh transform applied to it.
Args:
sample_shape (l... | Return a sample, sampled from this TanhNormal distribution.
Returns the sampled value before the tanh transform is applied and the
sampled value with the tanh transform applied to it.
Args:
sample_shape (list): shape of the return.
Note:
Gradients pass through ... | rsample_with_pre_tanh_value | python | rlworkgroup/garage | src/garage/torch/distributions/tanh_normal.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/torch/distributions/tanh_normal.py | MIT |
def _from_distribution(cls, new_normal):
"""Construct a new TanhNormal distribution from a normal distribution.
Args:
new_normal (Independent(Normal)): underlying normal dist for
the new TanhNormal distribution.
Returns:
TanhNormal: A new distribution wh... | Construct a new TanhNormal distribution from a normal distribution.
Args:
new_normal (Independent(Normal)): underlying normal dist for
the new TanhNormal distribution.
Returns:
TanhNormal: A new distribution whose underlying normal dist
is new_no... | _from_distribution | python | rlworkgroup/garage | src/garage/torch/distributions/tanh_normal.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/torch/distributions/tanh_normal.py | MIT |
def expand(self, batch_shape, _instance=None):
"""Returns a new TanhNormal distribution.
(or populates an existing instance provided by a derived class) with
batch dimensions expanded to `batch_shape`. This method calls
:class:`~torch.Tensor.expand` on the distribution's parameters. As
... | Returns a new TanhNormal distribution.
(or populates an existing instance provided by a derived class) with
batch dimensions expanded to `batch_shape`. This method calls
:class:`~torch.Tensor.expand` on the distribution's parameters. As
such, this does not allocate new memory for the ex... | expand | python | rlworkgroup/garage | src/garage/torch/distributions/tanh_normal.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/torch/distributions/tanh_normal.py | MIT |
def _clip_but_pass_gradient(x, lower=0., upper=1.):
"""Clipping function that allows for gradients to flow through.
Args:
x (torch.Tensor): value to be clipped
lower (float): lower bound of clipping
upper (float): upper bound of clipping
Returns:
... | Clipping function that allows for gradients to flow through.
Args:
x (torch.Tensor): value to be clipped
lower (float): lower bound of clipping
upper (float): upper bound of clipping
Returns:
torch.Tensor: x clipped between lower and upper.
| _clip_but_pass_gradient | python | rlworkgroup/garage | src/garage/torch/distributions/tanh_normal.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/torch/distributions/tanh_normal.py | MIT |
def spec(self):
"""garage.InOutSpec: Input and output space."""
input_space = akro.Box(-np.inf, np.inf, self._input_dim)
output_space = akro.Box(-np.inf, np.inf, self._output_dim)
return InOutSpec(input_space, output_space) | garage.InOutSpec: Input and output space. | spec | python | rlworkgroup/garage | src/garage/torch/embeddings/mlp_encoder.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/torch/embeddings/mlp_encoder.py | MIT |
def reset(self, do_resets=None):
"""Reset the encoder.
This is effective only to recurrent encoder. do_resets is effective
only to vectoried encoder.
For a vectorized encoder, do_resets is an array of boolean indicating
which internal states to be reset. The length of do_resets... | Reset the encoder.
This is effective only to recurrent encoder. do_resets is effective
only to vectoried encoder.
For a vectorized encoder, do_resets is an array of boolean indicating
which internal states to be reset. The length of do_resets should be
equal to the length of in... | reset | python | rlworkgroup/garage | src/garage/torch/embeddings/mlp_encoder.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/torch/embeddings/mlp_encoder.py | MIT |
def forward(self, x):
"""Forward method.
Args:
x (torch.Tensor): Input values. Should match image_format
specified at construction (either NCHW or NCWH).
Returns:
List[torch.Tensor]: Output values
"""
# Transform single values into batch... | Forward method.
Args:
x (torch.Tensor): Input values. Should match image_format
specified at construction (either NCHW or NCWH).
Returns:
List[torch.Tensor]: Output values
| forward | python | rlworkgroup/garage | src/garage/torch/modules/cnn_module.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/torch/modules/cnn_module.py | MIT |
def _check_spec(spec, image_format):
"""Check that an InOutSpec is suitable for a CNNModule.
Args:
spec (garage.InOutSpec): Specification of inputs and outputs. The
input should be in 'NCHW' format: [batch_size, channel, height,
width]. Will print a warning if the channel size... | Check that an InOutSpec is suitable for a CNNModule.
Args:
spec (garage.InOutSpec): Specification of inputs and outputs. The
input should be in 'NCHW' format: [batch_size, channel, height,
width]. Will print a warning if the channel size is not 1 or 3.
If output_space ... | _check_spec | python | rlworkgroup/garage | src/garage/torch/modules/cnn_module.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/torch/modules/cnn_module.py | MIT |
def to(self, *args, **kwargs):
"""Move the module to the specified device.
Args:
*args: args to pytorch to function.
**kwargs: keyword args to pytorch to function.
"""
super().to(*args, **kwargs)
buffers = dict(self.named_buffers())
if not isinst... | Move the module to the specified device.
Args:
*args: args to pytorch to function.
**kwargs: keyword args to pytorch to function.
| to | python | rlworkgroup/garage | src/garage/torch/modules/gaussian_mlp_module.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/torch/modules/gaussian_mlp_module.py | MIT |
def forward(self, *inputs):
"""Forward method.
Args:
*inputs: Input to the module.
Returns:
torch.distributions.independent.Independent: Independent
distribution.
"""
mean, log_std_uncentered = self._get_mean_and_log_std(*inputs)
... | Forward method.
Args:
*inputs: Input to the module.
Returns:
torch.distributions.independent.Independent: Independent
distribution.
| forward | python | rlworkgroup/garage | src/garage/torch/modules/gaussian_mlp_module.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/torch/modules/gaussian_mlp_module.py | MIT |
def _get_mean_and_log_std(self, x):
"""Get mean and std of Gaussian distribution given inputs.
Args:
x: Input to the module.
Returns:
torch.Tensor: The mean of Gaussian distribution.
torch.Tensor: The variance of Gaussian distribution.
"""
m... | Get mean and std of Gaussian distribution given inputs.
Args:
x: Input to the module.
Returns:
torch.Tensor: The mean of Gaussian distribution.
torch.Tensor: The variance of Gaussian distribution.
| _get_mean_and_log_std | python | rlworkgroup/garage | src/garage/torch/modules/gaussian_mlp_module.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/torch/modules/gaussian_mlp_module.py | MIT |
def _check_parameter_for_output_layer(cls, var_name, var, n_heads):
"""Check input parameters for output layer are valid.
Args:
var_name (str): variable name
var (any): variable to be checked
n_heads (int): number of head
Returns:
list: list of v... | Check input parameters for output layer are valid.
Args:
var_name (str): variable name
var (any): variable to be checked
n_heads (int): number of head
Returns:
list: list of variables (length of n_heads)
Raises:
ValueError: if the va... | _check_parameter_for_output_layer | python | rlworkgroup/garage | src/garage/torch/modules/multi_headed_mlp_module.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/torch/modules/multi_headed_mlp_module.py | MIT |
def forward(self, input_val):
"""Forward method.
Args:
input_val (torch.Tensor): Input values with (N, *, input_dim)
shape.
Returns:
List[torch.Tensor]: Output values
"""
x = input_val
for layer in self._layers:
x = l... | Forward method.
Args:
input_val (torch.Tensor): Input values with (N, *, input_dim)
shape.
Returns:
List[torch.Tensor]: Output values
| forward | python | rlworkgroup/garage | src/garage/torch/modules/multi_headed_mlp_module.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/torch/modules/multi_headed_mlp_module.py | MIT |
def _build_hessian_vector_product(func, params, reg_coeff=1e-5):
"""Computes Hessian-vector product using Pearlmutter's algorithm.
`Pearlmutter, Barak A. "Fast exact multiplication by the Hessian." Neural
computation 6.1 (1994): 147-160.`
Args:
func (callable): A function that returns a torch.... | Computes Hessian-vector product using Pearlmutter's algorithm.
`Pearlmutter, Barak A. "Fast exact multiplication by the Hessian." Neural
computation 6.1 (1994): 147-160.`
Args:
func (callable): A function that returns a torch.Tensor. Hessian of
the return value will be computed.
... | _build_hessian_vector_product | python | rlworkgroup/garage | src/garage/torch/optimizers/conjugate_gradient_optimizer.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/torch/optimizers/conjugate_gradient_optimizer.py | MIT |
def _eval(vector):
"""The evaluation function.
Args:
vector (torch.Tensor): The vector to be multiplied with
Hessian.
Returns:
torch.Tensor: The product of Hessian of function f and v.
"""
unflatten_vector = unflatten_tensors(vector, par... | The evaluation function.
Args:
vector (torch.Tensor): The vector to be multiplied with
Hessian.
Returns:
torch.Tensor: The product of Hessian of function f and v.
| _eval | python | rlworkgroup/garage | src/garage/torch/optimizers/conjugate_gradient_optimizer.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/torch/optimizers/conjugate_gradient_optimizer.py | MIT |
def _conjugate_gradient(f_Ax, b, cg_iters, residual_tol=1e-10):
"""Use Conjugate Gradient iteration to solve Ax = b. Demmel p 312.
Args:
f_Ax (callable): A function to compute Hessian vector product.
b (torch.Tensor): Right hand side of the equation to solve.
cg_iters (int): Number of i... | Use Conjugate Gradient iteration to solve Ax = b. Demmel p 312.
Args:
f_Ax (callable): A function to compute Hessian vector product.
b (torch.Tensor): Right hand side of the equation to solve.
cg_iters (int): Number of iterations to run conjugate gradient
algorithm.
resi... | _conjugate_gradient | python | rlworkgroup/garage | src/garage/torch/optimizers/conjugate_gradient_optimizer.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/torch/optimizers/conjugate_gradient_optimizer.py | MIT |
def step(self, f_loss, f_constraint): # pylint: disable=arguments-differ
"""Take an optimization step.
Args:
f_loss (callable): Function to compute the loss.
f_constraint (callable): Function to compute the constraint value.
"""
# Collect trainable parameters a... | Take an optimization step.
Args:
f_loss (callable): Function to compute the loss.
f_constraint (callable): Function to compute the constraint value.
| step | python | rlworkgroup/garage | src/garage/torch/optimizers/conjugate_gradient_optimizer.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/torch/optimizers/conjugate_gradient_optimizer.py | MIT |
def state(self):
"""dict: The hyper-parameters of the optimizer."""
return {
'max_constraint_value': self._max_constraint_value,
'cg_iters': self._cg_iters,
'max_backtracks': self._max_backtracks,
'backtrack_ratio': self._backtrack_ratio,
'hvp_... | dict: The hyper-parameters of the optimizer. | state | python | rlworkgroup/garage | src/garage/torch/optimizers/conjugate_gradient_optimizer.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/torch/optimizers/conjugate_gradient_optimizer.py | MIT |
def __setstate__(self, state):
"""Restore the optimizer state.
Args:
state (dict): State dictionary.
"""
if 'hvp_reg_coeff' not in state['state']:
warnings.warn(
'Resuming ConjugateGradientOptimizer with lost state. '
'This behavi... | Restore the optimizer state.
Args:
state (dict): State dictionary.
| __setstate__ | python | rlworkgroup/garage | src/garage/torch/optimizers/conjugate_gradient_optimizer.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/torch/optimizers/conjugate_gradient_optimizer.py | MIT |
def zero_grad(self):
"""Sets gradients of all model parameters to zero."""
for param in self.module.parameters():
if param.grad is not None:
param.grad.detach_()
param.grad.zero_() | Sets gradients of all model parameters to zero. | zero_grad | python | rlworkgroup/garage | src/garage/torch/optimizers/differentiable_sgd.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/torch/optimizers/differentiable_sgd.py | MIT |
def set_grads_none(self):
"""Sets gradients for all model parameters to None.
This is an alternative to `zero_grad` which sets
gradients to zero.
"""
for param in self.module.parameters():
if param.grad is not None:
param.grad = None | Sets gradients for all model parameters to None.
This is an alternative to `zero_grad` which sets
gradients to zero.
| set_grads_none | python | rlworkgroup/garage | src/garage/torch/optimizers/differentiable_sgd.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/torch/optimizers/differentiable_sgd.py | MIT |
def get_minibatch(self, *inputs):
r"""Yields a batch of inputs.
Notes: P is the size of minibatch (self._minibatch_size)
Args:
*inputs (list[torch.Tensor]): A list of inputs. Each input has
shape :math:`(N \dot [T], *)`.
Yields:
list[torch.Tenso... | Yields a batch of inputs.
Notes: P is the size of minibatch (self._minibatch_size)
Args:
*inputs (list[torch.Tensor]): A list of inputs. Each input has
shape :math:`(N \dot [T], *)`.
Yields:
list[torch.Tensor]: A list batch of inputs. Each batch has sha... | get_minibatch | python | rlworkgroup/garage | src/garage/torch/optimizers/optimizer_wrapper.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/torch/optimizers/optimizer_wrapper.py | MIT |
def forward(self, observations):
"""Compute the action distributions from the observations.
Args:
observations (torch.Tensor): Observations to act on.
Returns:
torch.distributions.Distribution: Batch distribution of actions.
dict[str, torch.Tensor]: Addition... | Compute the action distributions from the observations.
Args:
observations (torch.Tensor): Observations to act on.
Returns:
torch.distributions.Distribution: Batch distribution of actions.
dict[str, torch.Tensor]: Additional agent_info, as torch Tensors.
... | forward | python | rlworkgroup/garage | src/garage/torch/policies/categorical_cnn_policy.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/torch/policies/categorical_cnn_policy.py | MIT |
def reset_belief(self, num_tasks=1):
r"""Reset :math:`q(z \| c)` to the prior and sample a new z from the prior.
Args:
num_tasks (int): Number of tasks.
"""
# reset distribution over z to the prior
mu = torch.zeros(num_tasks, self._latent_dim).to(global_device())
... | Reset :math:`q(z \| c)` to the prior and sample a new z from the prior.
Args:
num_tasks (int): Number of tasks.
| reset_belief | python | rlworkgroup/garage | src/garage/torch/policies/context_conditioned_policy.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/torch/policies/context_conditioned_policy.py | MIT |
def sample_from_belief(self):
"""Sample z using distributions from current means and variances."""
if self._use_information_bottleneck:
posteriors = [
torch.distributions.Normal(m, torch.sqrt(s)) for m, s in zip(
torch.unbind(self.z_means), torch.unbind(se... | Sample z using distributions from current means and variances. | sample_from_belief | python | rlworkgroup/garage | src/garage/torch/policies/context_conditioned_policy.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/torch/policies/context_conditioned_policy.py | MIT |
def update_context(self, timestep):
"""Append single transition to the current context.
Args:
timestep (garage._dtypes.TimeStep): Timestep containing transition
information to be added to context.
"""
o = torch.as_tensor(timestep.observation[None, None, ...]... | Append single transition to the current context.
Args:
timestep (garage._dtypes.TimeStep): Timestep containing transition
information to be added to context.
| update_context | python | rlworkgroup/garage | src/garage/torch/policies/context_conditioned_policy.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/torch/policies/context_conditioned_policy.py | MIT |
def infer_posterior(self, context):
r"""Compute :math:`q(z \| c)` as a function of input context and sample new z.
Args:
context (torch.Tensor): Context values, with shape
:math:`(X, N, C)`. X is the number of tasks. N is batch size. C
is the combined size of... | Compute :math:`q(z \| c)` as a function of input context and sample new z.
Args:
context (torch.Tensor): Context values, with shape
:math:`(X, N, C)`. X is the number of tasks. N is batch size. C
is the combined size of observation, action, reward, and next
... | infer_posterior | python | rlworkgroup/garage | src/garage/torch/policies/context_conditioned_policy.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/torch/policies/context_conditioned_policy.py | MIT |
def forward(self, obs, context):
"""Given observations and context, get actions and probs from policy.
Args:
obs (torch.Tensor): Observation values, with shape
:math:`(X, N, O)`. X is the number of tasks. N is batch size. O
is the size of the flattened obser... | Given observations and context, get actions and probs from policy.
Args:
obs (torch.Tensor): Observation values, with shape
:math:`(X, N, O)`. X is the number of tasks. N is batch size. O
is the size of the flattened observation space.
context (torch.Ten... | forward | python | rlworkgroup/garage | src/garage/torch/policies/context_conditioned_policy.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/torch/policies/context_conditioned_policy.py | MIT |
def get_action(self, obs):
"""Sample action from the policy, conditioned on the task embedding.
Args:
obs (torch.Tensor): Observation values, with shape :math:`(1, O)`.
O is the size of the flattened observation space.
Returns:
torch.Tensor: Output actio... | Sample action from the policy, conditioned on the task embedding.
Args:
obs (torch.Tensor): Observation values, with shape :math:`(1, O)`.
O is the size of the flattened observation space.
Returns:
torch.Tensor: Output action value, with shape :math:`(1, A)`.
... | get_action | python | rlworkgroup/garage | src/garage/torch/policies/context_conditioned_policy.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/torch/policies/context_conditioned_policy.py | MIT |
def compute_kl_div(self):
r"""Compute :math:`KL(q(z|c) \| p(z))`.
Returns:
float: :math:`KL(q(z|c) \| p(z))`.
"""
prior = torch.distributions.Normal(
torch.zeros(self._latent_dim).to(global_device()),
torch.ones(self._latent_dim).to(global_device()))... | Compute :math:`KL(q(z|c) \| p(z))`.
Returns:
float: :math:`KL(q(z|c) \| p(z))`.
| compute_kl_div | python | rlworkgroup/garage | src/garage/torch/policies/context_conditioned_policy.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/torch/policies/context_conditioned_policy.py | MIT |
def __init__(self, env_spec, name='DeterministicMLPPolicy', **kwargs):
"""Initialize class with multiple attributes.
Args:
env_spec (EnvSpec): Environment specification.
name (str): Policy name.
**kwargs: Additional keyword arguments passed to the MLPModule.
... | Initialize class with multiple attributes.
Args:
env_spec (EnvSpec): Environment specification.
name (str): Policy name.
**kwargs: Additional keyword arguments passed to the MLPModule.
| __init__ | python | rlworkgroup/garage | src/garage/torch/policies/deterministic_mlp_policy.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/torch/policies/deterministic_mlp_policy.py | MIT |
def get_action(self, observation):
"""Get a single action given an observation.
Args:
observation (np.ndarray): Observation from the environment.
Returns:
tuple:
* np.ndarray: Predicted action.
* dict:
* np.ndarray[flo... | Get a single action given an observation.
Args:
observation (np.ndarray): Observation from the environment.
Returns:
tuple:
* np.ndarray: Predicted action.
* dict:
* np.ndarray[float]: Mean of the distribution
... | get_action | python | rlworkgroup/garage | src/garage/torch/policies/deterministic_mlp_policy.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/torch/policies/deterministic_mlp_policy.py | MIT |
def get_actions(self, observations):
"""Get actions given observations.
Args:
observations (np.ndarray): Observations from the environment.
Returns:
tuple:
* np.ndarray: Predicted actions.
* dict:
* np.ndarray[float]: ... | Get actions given observations.
Args:
observations (np.ndarray): Observations from the environment.
Returns:
tuple:
* np.ndarray: Predicted actions.
* dict:
* np.ndarray[float]: Mean of the distribution
* n... | get_actions | python | rlworkgroup/garage | src/garage/torch/policies/deterministic_mlp_policy.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/torch/policies/deterministic_mlp_policy.py | MIT |
def forward(self, observations):
"""Compute the action distributions from the observations.
Args:
observations(torch.Tensor): Batch of observations of shape
:math:`(N, O)`. Observations should be flattened even
if they are images as the underlying Q network h... | Compute the action distributions from the observations.
Args:
observations(torch.Tensor): Batch of observations of shape
:math:`(N, O)`. Observations should be flattened even
if they are images as the underlying Q network handles
unflattening.
... | forward | python | rlworkgroup/garage | src/garage/torch/policies/discrete_cnn_policy.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/torch/policies/discrete_cnn_policy.py | MIT |
def forward(self, observations):
"""Get actions corresponding to a batch of observations.
Args:
observations(torch.Tensor): Batch of observations of shape
:math:`(N, O)`. Observations should be flattened even
if they are images as the underlying Q network han... | Get actions corresponding to a batch of observations.
Args:
observations(torch.Tensor): Batch of observations of shape
:math:`(N, O)`. Observations should be flattened even
if they are images as the underlying Q network handles
unflattening.
... | forward | python | rlworkgroup/garage | src/garage/torch/policies/discrete_qf_argmax_policy.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/torch/policies/discrete_qf_argmax_policy.py | MIT |
def get_action(self, observation):
"""Get a single action given an observation.
Args:
observation (np.ndarray): Observation with shape :math:`(O, )`.
Returns:
torch.Tensor: Predicted action with shape :math:`(A, )`.
dict: Empty since this policy does not pro... | Get a single action given an observation.
Args:
observation (np.ndarray): Observation with shape :math:`(O, )`.
Returns:
torch.Tensor: Predicted action with shape :math:`(A, )`.
dict: Empty since this policy does not produce a distribution.
| get_action | python | rlworkgroup/garage | src/garage/torch/policies/discrete_qf_argmax_policy.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/torch/policies/discrete_qf_argmax_policy.py | MIT |
def get_actions(self, observations):
"""Get actions given observations.
Args:
observations (np.ndarray): Batch of observations, should
have shape :math:`(N, O)`.
Returns:
torch.Tensor: Predicted actions. Tensor has shape :math:`(N, A)`.
dict:... | Get actions given observations.
Args:
observations (np.ndarray): Batch of observations, should
have shape :math:`(N, O)`.
Returns:
torch.Tensor: Predicted actions. Tensor has shape :math:`(N, A)`.
dict: Empty since this policy does not produce a dist... | get_actions | python | rlworkgroup/garage | src/garage/torch/policies/discrete_qf_argmax_policy.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/torch/policies/discrete_qf_argmax_policy.py | MIT |
def forward(self, observations):
"""Compute the action distributions from the observations.
Args:
observations (torch.Tensor): Batch of observations on default
torch device.
Returns:
torch.distributions.Distribution: Batch distribution of actions.
... | Compute the action distributions from the observations.
Args:
observations (torch.Tensor): Batch of observations on default
torch device.
Returns:
torch.distributions.Distribution: Batch distribution of actions.
dict[str, torch.Tensor]: Additional ag... | forward | python | rlworkgroup/garage | src/garage/torch/policies/gaussian_mlp_policy.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/torch/policies/gaussian_mlp_policy.py | MIT |
def get_action(self, observation):
"""Get action sampled from the policy.
Args:
observation (np.ndarray): Observation from the environment.
Returns:
Tuple[np.ndarray, dict[str,np.ndarray]]: Action and extra agent
info.
""" | Get action sampled from the policy.
Args:
observation (np.ndarray): Observation from the environment.
Returns:
Tuple[np.ndarray, dict[str,np.ndarray]]: Action and extra agent
info.
| get_action | python | rlworkgroup/garage | src/garage/torch/policies/policy.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/torch/policies/policy.py | MIT |
def get_actions(self, observations):
"""Get actions given observations.
Args:
observations (np.ndarray): Observations from the environment.
Returns:
Tuple[np.ndarray, dict[str,np.ndarray]]: Actions and extra agent
infos.
""" | Get actions given observations.
Args:
observations (np.ndarray): Observations from the environment.
Returns:
Tuple[np.ndarray, dict[str,np.ndarray]]: Actions and extra agent
infos.
| get_actions | python | rlworkgroup/garage | src/garage/torch/policies/policy.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/torch/policies/policy.py | MIT |
Subsets and Splits
Django Code with Docstrings
Filters Python code examples from Django repository that contain Django-related code, helping identify relevant code snippets for understanding Django framework usage patterns.
SQL Console for Shuu12121/python-treesitter-filtered-datasetsV2
Retrieves Python code examples from Django repository that contain 'django' in the code, which helps identify Django-specific code snippets but provides limited analytical insights beyond basic filtering.
SQL Console for Shuu12121/python-treesitter-filtered-datasetsV2
Retrieves specific code examples from the Flask repository but doesn't provide meaningful analysis or patterns beyond basic data retrieval.
HTTPX Repo Code and Docstrings
Retrieves specific code examples from the httpx repository, which is useful for understanding how particular libraries are used but doesn't provide broader analytical insights about the dataset.
Requests Repo Docstrings & Code
Retrieves code examples with their docstrings and file paths from the requests repository, providing basic filtering but limited analytical value beyond finding specific code samples.
Quart Repo Docstrings & Code
Retrieves code examples with their docstrings from the Quart repository, providing basic code samples but offering limited analytical value for understanding broader patterns or relationships in the dataset.