"""This modules creates a deterministic policy network.
A neural network can be used as policy method in different RL algorithms.
It accepts an observation of the environment and predicts an action.
"""
import torch
from garage.torch.modules import MLPModule
from garage.torch.policies.policy import Policy
[docs]class DeterministicMLPPolicy(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).
"""
def __init__(self, env_spec, name='DeterministicMLPPolicy', **kwargs):
"""Initialize class with multiple attributes.
Args:
env_spec (garage.envs.env_spec.EnvSpec): Environment specification.
name (str): Policy name.
kwargs : Additional keyword arguments passed to the MLPModule.
"""
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 = MLPModule(input_dim=self._obs_dim,
output_dim=self._action_dim,
**kwargs)
# pylint: disable=arguments-differ
[docs] def forward(self, observations):
"""Compute actions from the observations.
Args:
observations (torch.Tensor): Batch of observations on default
torch device.
Returns:
torch.Tensor: Batch of actions.
"""
return self._module(observations)
[docs] def get_action(self, observation):
"""Get a single action given an observation.
Args:
observation (np.ndarray): Observation from the environment.
Returns:
tuple:
* np.ndarray: Predicted action.
* dict:
* list[float]: Mean of the distribution
* list[float]: Log of standard deviation of the
distribution
"""
with torch.no_grad():
x = self(torch.Tensor(observation).unsqueeze(0))
return x.squeeze(0).numpy(), dict()
[docs] def get_actions(self, observations):
"""Get actions given observations.
Args:
observations (np.ndarray): Observations from the environment.
Returns:
tuple:
* np.ndarray: Predicted actions.
* dict:
* list[float]: Mean of the distribution
* list[float]: Log of standard deviation of the
distribution
"""
with torch.no_grad():
x = self(torch.Tensor(observations))
return x.numpy(), dict()