"""GaussianMLPPolicy."""
import torch
from torch import nn
from garage.torch.modules import GaussianMLPModule
from garage.torch.policies.stochastic_policy import StochasticPolicy
[docs]class GaussianMLPPolicy(StochasticPolicy):
"""MLP whose outputs are fed into a Normal distribution..
A policy that contains a MLP to make prediction based on a gaussian
distribution.
Args:
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.
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.
"""
def __init__(self,
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-6,
max_std=None,
std_parameterization='exp',
layer_normalization=False,
name='GaussianMLPPolicy'):
super().__init__(env_spec, name)
self._obs_dim = env_spec.observation_space.flat_dim
self._action_dim = env_spec.action_space.flat_dim
self._module = GaussianMLPModule(
input_dim=self._obs_dim,
output_dim=self._action_dim,
hidden_sizes=hidden_sizes,
hidden_nonlinearity=hidden_nonlinearity,
hidden_w_init=hidden_w_init,
hidden_b_init=hidden_b_init,
output_nonlinearity=output_nonlinearity,
output_w_init=output_w_init,
output_b_init=output_b_init,
learn_std=learn_std,
init_std=init_std,
min_std=min_std,
max_std=max_std,
std_parameterization=std_parameterization,
layer_normalization=layer_normalization)
[docs] 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.
dict[str, torch.Tensor]: Additional agent_info, as torch Tensors
"""
dist = self._module(observations)
return (dist, dict(mean=dist.mean, log_std=(dist.variance**.5).log()))