garage.torch.policies

PyTorch Policies.

class CategoricalCNNPolicy(env, 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_nonlinearity=None, 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 (garage.envs) – Environment.
• 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_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.

name

Name of policy.

Returns:Name of policy Return type:str

env_spec

Policy environment specification.

Returns:Environment specification. Return type:garage.EnvSpec

observation_space

Observation space.

Returns:The observation space of the environment. Return type:akro.Space

action_space

Action space.

Returns:The action space of the environment. Return type:akro.Space

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.

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:tuple

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:tuple

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:dict

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.

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.

networks

Return context_encoder and policy.

Returns:Encoder and policy networks. Return type:list

context

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

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:

tuple

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:float

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).

name

Name of policy.

Returns:Name of policy Return type:str

env_spec

Policy environment specification.

Returns:Environment specification. Return type:garage.EnvSpec

observation_space

Observation space.

Returns:The observation space of the environment. Return type:akro.Space

action_space

Action space.

Returns:The action space of the environment. Return type:akro.Space

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:tuple

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:tuple

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:dict

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.

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.

name

Name of policy.

Returns:Name of policy Return type:str

env_spec

Policy environment specification.

Returns:Environment specification. Return type:garage.EnvSpec

observation_space

Observation space.

Returns:The observation space of the environment. Return type:akro.Space

action_space

Action space.

Returns:The action space of the environment. Return type:akro.Space

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:tuple

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:tuple

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:dict

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.

class Policy(env_spec, name)

Bases: torch.nn.Module, garage.np.policies.Policy, abc.ABC

Policy base class.

Parameters:

• env_spec (EnvSpec) – Environment specification.
• name (str) – Name of policy.

name

Name of policy.

Returns:Name of policy Return type:str

env_spec

Policy environment specification.

Returns:Environment specification. Return type:garage.EnvSpec

observation_space

Observation space.

Returns:The observation space of the environment. Return type:akro.Space

action_space

Action space.

Returns:The action space of the environment. Return type:akro.Space

get_action(self, observation)

Get action sampled from the policy.

Parameters:observation (np.ndarray) – Observation from the environment. Returns:

Action and extra agent

info.

Return type:Tuple[np.ndarray, dict[str,np.ndarray]]

get_actions(self, observations)

Get actions given observations.

Parameters:observations (np.ndarray) – Observations from the environment. Returns:

Actions and extra agent

infos.

Return type:Tuple[np.ndarray, dict[str,np.ndarray]]

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:dict

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.

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.

name

Name of policy.

Returns:Name of policy Return type:str

env_spec

Policy environment specification.

Returns:Environment specification. Return type:garage.EnvSpec

observation_space

Observation space.

Returns:The observation space of the environment. Return type:akro.Space

action_space

Action space.

Returns:The action space of the environment. Return type:akro.Space

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:tuple

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:tuple

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:dict

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.