garage.torch.policies
¶
PyTorch Policies.
- class CategoricalCNNPolicy(env_spec, image_format, kernel_sizes, *, hidden_channels, strides=1, hidden_sizes=(32, 32), hidden_nonlinearity=torch.tanh, hidden_w_init=nn.init.xavier_uniform_, hidden_b_init=nn.init.zeros_, paddings=0, padding_mode='zeros', max_pool=False, pool_shape=None, pool_stride=1, output_w_init=nn.init.xavier_uniform_, output_b_init=nn.init.zeros_, layer_normalization=False, name='CategoricalCNNPolicy')¶
Bases:
garage.torch.policies.stochastic_policy.StochasticPolicy
CategoricalCNNPolicy.
A policy that contains a CNN and a MLP to make prediction based on a categorical distribution.
It only works with akro.Discrete action space.
- Parameters
env_spec (garage.EnvSpec) – Environment specification.
image_format (str) – Either ‘NCHW’ or ‘NHWC’. Should match env_spec. Gym uses NHWC by default, but PyTorch uses NCHW by default.
kernel_sizes (tuple[int]) – Dimension of the conv filters. For example, (3, 5) means there are two convolutional layers. The filter for first layer is of dimension (3 x 3) and the second one is of dimension (5 x 5).
strides (tuple[int]) – The stride of the sliding window. For example, (1, 2) means there are two convolutional layers. The stride of the filter for first layer is 1 and that of the second layer is 2.
hidden_channels (tuple[int]) – Number of output channels for CNN. For example, (3, 32) means there are two convolutional layers. The filter for the first conv layer outputs 3 channels
hidden_sizes (list[int]) – Output dimension of dense layer(s) for the MLP for mean. For example, (32, 32) means the MLP consists of two hidden layers, each with 32 hidden units.
hidden_nonlinearity (callable) – Activation function for intermediate dense layer(s). It should return a torch.Tensor. Set it to None to maintain a linear activation.
hidden_w_init (callable) – Initializer function for the weight of intermediate dense layer(s). The function should return a torch.Tensor.
hidden_b_init (callable) – Initializer function for the bias of intermediate dense layer(s). The function should return a torch.Tensor.
paddings (tuple[int]) – Zero-padding added to both sides of the input
padding_mode (str) – The type of padding algorithm to use, either ‘SAME’ or ‘VALID’.
max_pool (bool) – Bool for using max-pooling or not.
pool_shape (tuple[int]) – Dimension of the pooling layer(s). For example, (2, 2) means that all the pooling layers have shape (2, 2).
pool_stride (tuple[int]) – The strides of the pooling layer(s). For example, (2, 2) means that all the pooling layers have strides (2, 2).
output_w_init (callable) – Initializer function for the weight of output dense layer(s). The function should return a torch.Tensor.
output_b_init (callable) – Initializer function for the bias of output dense layer(s). The function should return a torch.Tensor.
layer_normalization (bool) – Bool for using layer normalization or not.
name (str) – Name of policy.
- forward(self, observations)¶
Compute the action distributions from the observations.
- Parameters
observations (torch.Tensor) – Observations to act on.
- Returns
Batch distribution of actions. dict[str, torch.Tensor]: Additional agent_info, as torch Tensors.
Do not need to be detached, and can be on any device.
- Return type
torch.distributions.Distribution
- get_action(self, observation)¶
Get a single action given an observation.
- Parameters
observation (np.ndarray) – Observation from the environment. Shape is \(env_spec.observation_space\).
- Returns
- np.ndarray: Predicted action. Shape is
\(env_spec.action_space\).
- dict:
np.ndarray[float]: Mean of the distribution
- np.ndarray[float]: Standard deviation of logarithmic
values of the distribution.
- Return type
- get_actions(self, observations)¶
Get actions given observations.
- Parameters
observations (np.ndarray) – Observations from the environment. Shape is \(batch_dim \bullet env_spec.observation_space\).
- Returns
- np.ndarray: Predicted actions.
\(batch_dim \bullet env_spec.action_space\).
- dict:
np.ndarray[float]: Mean of the distribution.
- np.ndarray[float]: Standard deviation of logarithmic
values of the distribution.
- Return type
- get_param_values(self)¶
Get the parameters to the policy.
This method is included to ensure consistency with TF policies.
- Returns
The parameters (in the form of the state dictionary).
- Return type
- set_param_values(self, state_dict)¶
Set the parameters to the policy.
This method is included to ensure consistency with TF policies.
- Parameters
state_dict (dict) – State dictionary.
- reset(self, do_resets=None)¶
Reset the policy.
This is effective only to recurrent policies.
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 inputs, i.e. batch size.
- Parameters
do_resets (numpy.ndarray) – Bool array indicating which states to be reset.
- property env_spec(self)¶
Policy environment specification.
- Returns
Environment specification.
- Return type
- property observation_space(self)¶
Observation space.
- Returns
The observation space of the environment.
- Return type
akro.Space
- property action_space(self)¶
Action space.
- Returns
The action space of the environment.
- Return type
akro.Space
- class ContextConditionedPolicy(latent_dim, context_encoder, policy, use_information_bottleneck, use_next_obs)¶
Bases:
torch.nn.Module
A policy that outputs actions based on observation and latent context.
In PEARL, policies are conditioned on current state and a latent context (adaptation data) variable Z. This inference network estimates the posterior probability of z given past transitions. It uses context information stored in the encoder to infer the probabilistic value of z and samples from a policy conditioned on z.
- Parameters
latent_dim (int) – Latent context variable dimension.
context_encoder (garage.torch.embeddings.ContextEncoder) – Recurrent or permutation-invariant context encoder.
policy (garage.torch.policies.Policy) – Policy used to train the network.
use_information_bottleneck (bool) – True if latent context is not deterministic; false otherwise.
use_next_obs (bool) – True if next observation is used in context for distinguishing tasks; false otherwise.
- reset_belief(self, num_tasks=1)¶
Reset \(q(z \| c)\) to the prior and sample a new z from the prior.
- Parameters
num_tasks (int) – Number of tasks.
- sample_from_belief(self)¶
Sample z using distributions from current means and variances.
- update_context(self, timestep)¶
Append single transition to the current context.
- Parameters
timestep (garage._dtypes.TimeStep) – Timestep containing transition information to be added to context.
- infer_posterior(self, context)¶
Compute \(q(z \| c)\) as a function of input context and sample new z.
- Parameters
context (torch.Tensor) – Context values, with shape \((X, N, C)\). X is the number of tasks. N is batch size. C is the combined size of observation, action, reward, and next observation if next observation is used in context. Otherwise, C is the combined size of observation, action, and reward.
- forward(self, obs, context)¶
Given observations and context, get actions and probs from policy.
- Parameters
obs (torch.Tensor) –
Observation values, with shape \((X, N, O)\). X is the number of tasks. N is batch size. O
is the size of the flattened observation space.
context (torch.Tensor) – Context values, with shape \((X, N, C)\). X is the number of tasks. N is batch size. C is the combined size of observation, action, reward, and next observation if next observation is used in context. Otherwise, C is the combined size of observation, action, and reward.
- Returns
- torch.Tensor: Predicted action values.
np.ndarray: Mean of distribution.
np.ndarray: Log std of distribution.
torch.Tensor: Log likelihood of distribution.
- torch.Tensor: Sampled values from distribution before
applying tanh transformation.
- torch.Tensor: z values, with shape \((N, L)\). N is batch size.
L is the latent dimension.
- Return type
- get_action(self, obs)¶
Sample action from the policy, conditioned on the task embedding.
- Parameters
obs (torch.Tensor) – Observation values, with shape \((1, O)\). O is the size of the flattened observation space.
- Returns
- Output action value, with shape \((1, A)\).
A is the size of the flattened action space.
- dict:
np.ndarray[float]: Mean of the distribution.
- np.ndarray[float]: Standard deviation of logarithmic values
of the distribution.
- Return type
torch.Tensor
- compute_kl_div(self)¶
Compute \(KL(q(z|c) \| p(z))\).
- Returns
\(KL(q(z|c) \| p(z))\).
- Return type
- property networks(self)¶
Return context_encoder and policy.
- Returns
Encoder and policy networks.
- Return type
- property context(self)¶
Return context.
- Returns
- Context values, with shape \((X, N, C)\).
X is the number of tasks. N is batch size. C is the combined size of observation, action, reward, and next observation if next observation is used in context. Otherwise, C is the combined size of observation, action, and reward.
- Return type
torch.Tensor
- class DeterministicMLPPolicy(env_spec, name='DeterministicMLPPolicy', **kwargs)¶
Bases:
garage.torch.policies.policy.Policy
Implements a deterministic policy network.
The policy network selects action based on the state of the environment. It uses a PyTorch neural network module to fit the function of pi(s).
- forward(self, observations)¶
Compute actions from the observations.
- Parameters
observations (torch.Tensor) – Batch of observations on default torch device.
- Returns
Batch of actions.
- Return type
torch.Tensor
- get_action(self, observation)¶
Get a single action given an observation.
- Parameters
observation (np.ndarray) – Observation from the environment.
- Returns
np.ndarray: Predicted action.
- dict:
np.ndarray[float]: Mean of the distribution
- np.ndarray[float]: Log of standard deviation of the
distribution
- Return type
- get_actions(self, observations)¶
Get actions given observations.
- Parameters
observations (np.ndarray) – Observations from the environment.
- Returns
np.ndarray: Predicted actions.
- dict:
np.ndarray[float]: Mean of the distribution
- np.ndarray[float]: Log of standard deviation of the
distribution
- Return type
- get_param_values(self)¶
Get the parameters to the policy.
This method is included to ensure consistency with TF policies.
- Returns
The parameters (in the form of the state dictionary).
- Return type
- set_param_values(self, state_dict)¶
Set the parameters to the policy.
This method is included to ensure consistency with TF policies.
- Parameters
state_dict (dict) – State dictionary.
- reset(self, do_resets=None)¶
Reset the policy.
This is effective only to recurrent policies.
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 inputs, i.e. batch size.
- Parameters
do_resets (numpy.ndarray) – Bool array indicating which states to be reset.
- property env_spec(self)¶
Policy environment specification.
- Returns
Environment specification.
- Return type
- property observation_space(self)¶
Observation space.
- Returns
The observation space of the environment.
- Return type
akro.Space
- property action_space(self)¶
Action space.
- Returns
The action space of the environment.
- Return type
akro.Space
- class DiscreteCNNPolicy(env_spec, image_format, kernel_sizes, hidden_channels, strides, hidden_sizes=(32, 32), cnn_hidden_nonlinearity=torch.nn.ReLU, mlp_hidden_nonlinearity=torch.nn.ReLU, hidden_w_init=nn.init.xavier_uniform_, hidden_b_init=nn.init.zeros_, paddings=0, padding_mode='zeros', max_pool=False, pool_shape=None, pool_stride=1, output_nonlinearity=None, output_w_init=nn.init.xavier_uniform_, output_b_init=nn.init.zeros_, layer_normalization=False, name='DiscreteCNNPolicy')¶
Bases:
garage.torch.policies.stochastic_policy.StochasticPolicy
DiscreteCNNPolicy.
A policy that contains a CNN and a MLP to make prediction based on a discrete distribution.
- Parameters
env_spec (EnvSpec) – Environment specification.
image_format (str) – Either ‘NCHW’ or ‘NHWC’. Should match env_spec. Gym uses NHWC by default, but PyTorch uses NCHW by default.
kernel_sizes (tuple[int]) – Dimension of the conv filters. For example, (3, 5) means there are two convolutional layers. The filter for first layer is of dimension (3 x 3) and the second one is of dimension (5 x 5).
strides (tuple[int]) – The stride of the sliding window. For example, (1, 2) means there are two convolutional layers. The stride of the filter for first layer is 1 and that of the second layer is 2.
hidden_channels (tuple[int]) – Number of output channels for CNN. For example, (3, 32) means there are two convolutional layers. The filter for the first conv layer outputs 3 channels
hidden_sizes (list[int]) – Output dimension of dense layer(s) for the MLP for mean. For example, (32, 32) means the MLP consists of two hidden layers, each with 32 hidden units.
mlp_hidden_nonlinearity (callable) – Activation function for intermediate dense layer(s) in the MLP. It should return a torch.Tensor. Set it to None to maintain a linear activation.
cnn_hidden_nonlinearity (callable) – Activation function for intermediate CNN layer(s). It should return a torch.Tensor. Set it to None to maintain a linear activation.
hidden_w_init (callable) – Initializer function for the weight of intermediate dense layer(s). The function should return a torch.Tensor.
hidden_b_init (callable) – Initializer function for the bias of intermediate dense layer(s). The function should return a torch.Tensor.
paddings (tuple[int]) – Zero-padding added to both sides of the input
padding_mode (str) – The type of padding algorithm to use, either ‘SAME’ or ‘VALID’.
max_pool (bool) – Bool for using max-pooling or not.
pool_shape (tuple[int]) – Dimension of the pooling layer(s). For example, (2, 2) means that all the pooling layers have shape (2, 2).
pool_stride (tuple[int]) – The strides of the pooling layer(s). For example, (2, 2) means that all the pooling layers have strides (2, 2).
output_nonlinearity (callable) – Activation function for output dense layer. It should return a torch.Tensor. Set it to None to maintain a linear activation.
output_w_init (callable) – Initializer function for the weight of output dense layer(s). The function should return a torch.Tensor.
output_b_init (callable) – Initializer function for the bias of output dense layer(s). The function should return a torch.Tensor.
layer_normalization (bool) – Bool for using layer normalization or not.
name (str) – Name of policy.
- forward(self, observations)¶
Compute the action distributions from the observations.
- Parameters
observations (torch.Tensor) – Batch of observations of shape \((N, O)\). Observations should be flattened even if they are images as the underlying Q network handles unflattening.
- Returns
Batch distribution of actions. dict[str, torch.Tensor]: Additional agent_info, as torch Tensors.
Do not need to be detached, and can be on any device.
- Return type
torch.distributions.Distribution
- get_action(self, observation)¶
Get a single action given an observation.
- Parameters
observation (np.ndarray) – Observation from the environment. Shape is \(env_spec.observation_space\).
- Returns
- np.ndarray: Predicted action. Shape is
\(env_spec.action_space\).
- dict:
np.ndarray[float]: Mean of the distribution
- np.ndarray[float]: Standard deviation of logarithmic
values of the distribution.
- Return type
- get_actions(self, observations)¶
Get actions given observations.
- Parameters
observations (np.ndarray) – Observations from the environment. Shape is \(batch_dim \bullet env_spec.observation_space\).
- Returns
- np.ndarray: Predicted actions.
\(batch_dim \bullet env_spec.action_space\).
- dict:
np.ndarray[float]: Mean of the distribution.
- np.ndarray[float]: Standard deviation of logarithmic
values of the distribution.
- Return type
- get_param_values(self)¶
Get the parameters to the policy.
This method is included to ensure consistency with TF policies.
- Returns
The parameters (in the form of the state dictionary).
- Return type
- set_param_values(self, state_dict)¶
Set the parameters to the policy.
This method is included to ensure consistency with TF policies.
- Parameters
state_dict (dict) – State dictionary.
- reset(self, do_resets=None)¶
Reset the policy.
This is effective only to recurrent policies.
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 inputs, i.e. batch size.
- Parameters
do_resets (numpy.ndarray) – Bool array indicating which states to be reset.
- property env_spec(self)¶
Policy environment specification.
- Returns
Environment specification.
- Return type
- property observation_space(self)¶
Observation space.
- Returns
The observation space of the environment.
- Return type
akro.Space
- property action_space(self)¶
Action space.
- Returns
The action space of the environment.
- Return type
akro.Space
- class DiscreteQFArgmaxPolicy(qf, env_spec, name='DiscreteQFArgmaxPolicy')¶
Bases:
garage.torch.policies.policy.Policy
Policy that derives its actions from a learned Q function.
The action returned is the one that yields the highest Q value for a given state, as determined by the supplied Q function.
- Parameters
- forward(self, observations)¶
Get actions corresponding to a batch of observations.
- Parameters
observations (torch.Tensor) – Batch of observations of shape \((N, O)\). Observations should be flattened even if they are images as the underlying Q network handles unflattening.
- Returns
Batch of actions of shape \((N, A)\)
- Return type
torch.Tensor
- get_action(self, observation)¶
Get a single action given an observation.
- Parameters
observation (np.ndarray) – Observation with shape \((O, )\).
- Returns
Predicted action with shape \((A, )\). dict: Empty since this policy does not produce a distribution.
- Return type
torch.Tensor
- get_actions(self, observations)¶
Get actions given observations.
- Parameters
observations (np.ndarray) – Batch of observations, should have shape \((N, O)\).
- Returns
Predicted actions. Tensor has shape \((N, A)\). dict: Empty since this policy does not produce a distribution.
- Return type
torch.Tensor
- get_param_values(self)¶
Get the parameters to the policy.
This method is included to ensure consistency with TF policies.
- Returns
The parameters (in the form of the state dictionary).
- Return type
- set_param_values(self, state_dict)¶
Set the parameters to the policy.
This method is included to ensure consistency with TF policies.
- Parameters
state_dict (dict) – State dictionary.
- reset(self, do_resets=None)¶
Reset the policy.
This is effective only to recurrent policies.
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 inputs, i.e. batch size.
- Parameters
do_resets (numpy.ndarray) – Bool array indicating which states to be reset.
- property env_spec(self)¶
Policy environment specification.
- Returns
Environment specification.
- Return type
- property observation_space(self)¶
Observation space.
- Returns
The observation space of the environment.
- Return type
akro.Space
- property action_space(self)¶
Action space.
- Returns
The action space of the environment.
- Return type
akro.Space
- class GaussianMLPPolicy(env_spec, hidden_sizes=(32, 32), hidden_nonlinearity=torch.tanh, hidden_w_init=nn.init.xavier_uniform_, hidden_b_init=nn.init.zeros_, output_nonlinearity=None, output_w_init=nn.init.xavier_uniform_, output_b_init=nn.init.zeros_, learn_std=True, init_std=1.0, min_std=1e-06, max_std=None, std_parameterization='exp', layer_normalization=False, name='GaussianMLPPolicy')¶
Bases:
garage.torch.policies.stochastic_policy.StochasticPolicy
MLP whose outputs are fed into a Normal distribution..
A policy that contains a MLP to make prediction based on a gaussian distribution.
- Parameters
env_spec (EnvSpec) – Environment specification.
hidden_sizes (list[int]) – Output dimension of dense layer(s) for the MLP for mean. For example, (32, 32) means the MLP consists of two hidden layers, each with 32 hidden units.
hidden_nonlinearity (callable) – Activation function for intermediate dense layer(s). It should return a torch.Tensor. Set it to None to maintain a linear activation.
hidden_w_init (callable) – Initializer function for the weight of intermediate dense layer(s). The function should return a torch.Tensor.
hidden_b_init (callable) – Initializer function for the bias of intermediate dense layer(s). The function should return a torch.Tensor.
output_nonlinearity (callable) – Activation function for output dense layer. It should return a torch.Tensor. Set it to None to maintain a linear activation.
output_w_init (callable) – Initializer function for the weight of output dense layer(s). The function should return a torch.Tensor.
output_b_init (callable) – Initializer function for the bias of output dense layer(s). The function should return a torch.Tensor.
learn_std (bool) – Is std trainable.
init_std (float) – Initial value for std. (plain value - not log or exponentiated).
min_std (float) – Minimum value for std.
max_std (float) – Maximum value for std.
std_parameterization (str) –
How the std should be parametrized. There are two options: - exp: the logarithm of the std will be stored, and applied a
exponential transformation
softplus: the std will be computed as log(1+exp(x))
layer_normalization (bool) – Bool for using layer normalization or not.
name (str) – Name of policy.
- forward(self, observations)¶
Compute the action distributions from the observations.
- Parameters
observations (torch.Tensor) – Batch of observations on default torch device.
- Returns
Batch distribution of actions. dict[str, torch.Tensor]: Additional agent_info, as torch Tensors
- Return type
torch.distributions.Distribution
- get_action(self, observation)¶
Get a single action given an observation.
- Parameters
observation (np.ndarray) – Observation from the environment. Shape is \(env_spec.observation_space\).
- Returns
- np.ndarray: Predicted action. Shape is
\(env_spec.action_space\).
- dict:
np.ndarray[float]: Mean of the distribution
- np.ndarray[float]: Standard deviation of logarithmic
values of the distribution.
- Return type
- get_actions(self, observations)¶
Get actions given observations.
- Parameters
observations (np.ndarray) – Observations from the environment. Shape is \(batch_dim \bullet env_spec.observation_space\).
- Returns
- np.ndarray: Predicted actions.
\(batch_dim \bullet env_spec.action_space\).
- dict:
np.ndarray[float]: Mean of the distribution.
- np.ndarray[float]: Standard deviation of logarithmic
values of the distribution.
- Return type
- get_param_values(self)¶
Get the parameters to the policy.
This method is included to ensure consistency with TF policies.
- Returns
The parameters (in the form of the state dictionary).
- Return type
- set_param_values(self, state_dict)¶
Set the parameters to the policy.
This method is included to ensure consistency with TF policies.
- Parameters
state_dict (dict) – State dictionary.
- reset(self, do_resets=None)¶
Reset the policy.
This is effective only to recurrent policies.
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 inputs, i.e. batch size.
- Parameters
do_resets (numpy.ndarray) – Bool array indicating which states to be reset.
- property env_spec(self)¶
Policy environment specification.
- Returns
Environment specification.
- Return type
- property observation_space(self)¶
Observation space.
- Returns
The observation space of the environment.
- Return type
akro.Space
- property action_space(self)¶
Action space.
- Returns
The action space of the environment.
- Return type
akro.Space
- class Policy(env_spec, name)¶
Bases:
torch.nn.Module
,garage.np.policies.Policy
,abc.ABC
Policy base class.
- abstract get_action(self, observation)¶
Get action sampled from the policy.
- abstract get_actions(self, observations)¶
Get actions given observations.
- get_param_values(self)¶
Get the parameters to the policy.
This method is included to ensure consistency with TF policies.
- Returns
The parameters (in the form of the state dictionary).
- Return type
- set_param_values(self, state_dict)¶
Set the parameters to the policy.
This method is included to ensure consistency with TF policies.
- Parameters
state_dict (dict) – State dictionary.
- reset(self, do_resets=None)¶
Reset the policy.
This is effective only to recurrent policies.
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 inputs, i.e. batch size.
- Parameters
do_resets (numpy.ndarray) – Bool array indicating which states to be reset.
- property env_spec(self)¶
Policy environment specification.
- Returns
Environment specification.
- Return type
- property observation_space(self)¶
Observation space.
- Returns
The observation space of the environment.
- Return type
akro.Space
- property action_space(self)¶
Action space.
- Returns
The action space of the environment.
- Return type
akro.Space
- class TanhGaussianMLPPolicy(env_spec, hidden_sizes=(32, 32), hidden_nonlinearity=nn.ReLU, hidden_w_init=nn.init.xavier_uniform_, hidden_b_init=nn.init.zeros_, output_nonlinearity=None, output_w_init=nn.init.xavier_uniform_, output_b_init=nn.init.zeros_, init_std=1.0, min_std=np.exp(- 20.0), max_std=np.exp(2.0), std_parameterization='exp', layer_normalization=False)¶
Bases:
garage.torch.policies.stochastic_policy.StochasticPolicy
Multiheaded MLP whose outputs are fed into a TanhNormal distribution.
A policy that contains a MLP to make prediction based on a gaussian distribution with a tanh transformation.
- Parameters
env_spec (EnvSpec) – Environment specification.
hidden_sizes (list[int]) – Output dimension of dense layer(s) for the MLP for mean. For example, (32, 32) means the MLP consists of two hidden layers, each with 32 hidden units.
hidden_nonlinearity (callable) – Activation function for intermediate dense layer(s). It should return a torch.Tensor. Set it to None to maintain a linear activation.
hidden_w_init (callable) – Initializer function for the weight of intermediate dense layer(s). The function should return a torch.Tensor.
hidden_b_init (callable) – Initializer function for the bias of intermediate dense layer(s). The function should return a torch.Tensor.
output_nonlinearity (callable) – Activation function for output dense layer. It should return a torch.Tensor. Set it to None to maintain a linear activation.
output_w_init (callable) – Initializer function for the weight of output dense layer(s). The function should return a torch.Tensor.
output_b_init (callable) – Initializer function for the bias of output dense layer(s). The function should return a torch.Tensor.
init_std (float) – Initial value for std. (plain value - not log or exponentiated).
min_std (float) – If not None, the std is at least the value of min_std, to avoid numerical issues (plain value - not log or exponentiated).
max_std (float) – If not None, the std is at most the value of max_std, to avoid numerical issues (plain value - not log or exponentiated).
std_parameterization (str) –
How the std should be parametrized. There are two options: - exp: the logarithm of the std will be stored, and applied a
exponential transformation
softplus: the std will be computed as log(1+exp(x))
layer_normalization (bool) – Bool for using layer normalization or not.
- forward(self, observations)¶
Compute the action distributions from the observations.
- Parameters
observations (torch.Tensor) – Batch of observations on default torch device.
- Returns
Batch distribution of actions. dict[str, torch.Tensor]: Additional agent_info, as torch Tensors
- Return type
torch.distributions.Distribution
- get_action(self, observation)¶
Get a single action given an observation.
- Parameters
observation (np.ndarray) – Observation from the environment. Shape is \(env_spec.observation_space\).
- Returns
- np.ndarray: Predicted action. Shape is
\(env_spec.action_space\).
- dict:
np.ndarray[float]: Mean of the distribution
- np.ndarray[float]: Standard deviation of logarithmic
values of the distribution.
- Return type
- get_actions(self, observations)¶
Get actions given observations.
- Parameters
observations (np.ndarray) – Observations from the environment. Shape is \(batch_dim \bullet env_spec.observation_space\).
- Returns
- np.ndarray: Predicted actions.
\(batch_dim \bullet env_spec.action_space\).
- dict:
np.ndarray[float]: Mean of the distribution.
- np.ndarray[float]: Standard deviation of logarithmic
values of the distribution.
- Return type
- get_param_values(self)¶
Get the parameters to the policy.
This method is included to ensure consistency with TF policies.
- Returns
The parameters (in the form of the state dictionary).
- Return type
- set_param_values(self, state_dict)¶
Set the parameters to the policy.
This method is included to ensure consistency with TF policies.
- Parameters
state_dict (dict) – State dictionary.
- reset(self, do_resets=None)¶
Reset the policy.
This is effective only to recurrent policies.
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 inputs, i.e. batch size.
- Parameters
do_resets (numpy.ndarray) – Bool array indicating which states to be reset.
- property env_spec(self)¶
Policy environment specification.
- Returns
Environment specification.
- Return type
- property observation_space(self)¶
Observation space.
- Returns
The observation space of the environment.
- Return type
akro.Space
- property action_space(self)¶
Action space.
- Returns
The action space of the environment.
- Return type
akro.Space