garage.torch.policies.tanh_gaussian_mlp_policy

TanhGaussianMLPPolicy.

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

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.

property name(self)

Name of policy.

Returns

Name of policy

Return type

str

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

garage.EnvSpec

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