garage.torch.policies.tanh_gaussian_mlp_policy module¶
TanhGaussianMLPPolicy.
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class
TanhGaussianMLPPolicy
(env_spec, hidden_sizes=(32, 32), hidden_nonlinearity=<sphinx.ext.autodoc.importer._MockObject object>, hidden_w_init=<sphinx.ext.autodoc.importer._MockObject object>, hidden_b_init=<sphinx.ext.autodoc.importer._MockObject object>, output_nonlinearity=None, output_w_init=<sphinx.ext.autodoc.importer._MockObject object>, output_b_init=<sphinx.ext.autodoc.importer._MockObject object>, init_std=1.0, min_std=2.061153622438558e-09, max_std=7.38905609893065, std_parameterization='exp', layer_normalization=False)[source]¶ 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 (garage.envs.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.
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forward
(observations)[source]¶ 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