def __init__(
self, spec: EnvSpec, feats: FeatureStack, init_param_kwargs: Optional[dict] = None, use_cuda: bool = False
):
"""
Constructor
:param spec: specification of environment
:param feats: list of feature functions
:param init_param_kwargs: additional keyword arguments for the policy parameter initialization
:param use_cuda: `True` to move the module to the GPU, `False` (default) to use the CPU
"""
if not isinstance(feats, FeatureStack):
raise pyrado.TypeErr(given=feats, expected_type=FeatureStack)
# Call Policy's constructor
super().__init__(spec, use_cuda)
self._feats = feats
self.num_active_feat = feats.get_num_feat(spec.obs_space.flat_dim)
self.net = nn.Linear(self.num_active_feat, spec.act_space.flat_dim, bias=False)
# Call custom initialization function after PyTorch network parameter initialization
init_param_kwargs = init_param_kwargs if init_param_kwargs is not None else dict()
self.init_param(None, **init_param_kwargs)
self.to(self.device)
def __init__(self,
spec: EnvSpec,
feats: FeatureStack,
init_param_kwargs: dict = None,
use_cuda: bool = False):
"""
Constructor
:param spec: specification of environment
:param feats: list of feature functions
:param init_param_kwargs: additional keyword arguments for the policy parameter initialization
"""
super().__init__(spec, use_cuda)
if not isinstance(feats, FeatureStack):
raise pyrado.TypeErr(given=feats, expected_type=FeatureStack)
# Store inputs
self._num_act = spec.act_space.flat_dim
self._num_obs = spec.obs_space.flat_dim
self._feats = feats
self.num_active_feat = feats.get_num_feat(self._num_obs)
self.net = nn.Linear(self.num_active_feat, self._num_act, bias=False)
# Call custom initialization function after PyTorch network parameter initialization
init_param_kwargs = init_param_kwargs if init_param_kwargs is not None else dict(
)
self.init_param(None, **init_param_kwargs)
self.to(self.device)
