Function _make_spec

advanced_source/pendulum.py:404–435

(self, td_params)

def _make_spec(self, td_params):
    # Under the hood, this will populate self.output_spec["observation"]
    self.observation_spec = CompositeSpec(
        th=BoundedTensorSpec(
            low=-torch.pi,
            high=torch.pi,
            shape=(),
            dtype=torch.float32,
        ),
        thdot=BoundedTensorSpec(
            low=-td_params["params", "max_speed"],
            high=td_params["params", "max_speed"],
            shape=(),
            dtype=torch.float32,
        ),
        # we need to add the ``params`` to the observation specs, as we want
        # to pass it at each step during a rollout
        params=make_composite_from_td(td_params["params"]),
        shape=(),
    )
    # since the environment is stateless, we expect the previous output as input.
    # For this, ``EnvBase`` expects some state_spec to be available
    self.state_spec = self.observation_spec.clone()
    # action-spec will be automatically wrapped in input_spec when
    # `self.action_spec = spec` will be called supported
    self.action_spec = BoundedTensorSpec(
        low=-td_params["params", "max_torque"],
        high=td_params["params", "max_torque"],
        shape=(1,),
        dtype=torch.float32,
    )
    self.reward_spec = UnboundedContinuousTensorSpec(shape=(*td_params.shape, 1))


def make_composite_from_td(td):