Function _reset

advanced_source/pendulum.py:312–345

(self, tensordict)

def _reset(self, tensordict):
    if tensordict is None or tensordict.is_empty():
        # if no ``tensordict`` is passed, we generate a single set of hyperparameters
        # Otherwise, we assume that the input ``tensordict`` contains all the relevant
        # parameters to get started.
        tensordict = self.gen_params(batch_size=self.batch_size)

    high_th = torch.tensor(DEFAULT_X, device=self.device)
    high_thdot = torch.tensor(DEFAULT_Y, device=self.device)
    low_th = -high_th
    low_thdot = -high_thdot

    # for non batch-locked environments, the input ``tensordict`` shape dictates the number
    # of simulators run simultaneously. In other contexts, the initial
    # random state's shape will depend upon the environment batch-size instead.
    th = (
        torch.rand(tensordict.shape, generator=self.rng, device=self.device)
        * (high_th - low_th)
        + low_th
    )
    thdot = (
        torch.rand(tensordict.shape, generator=self.rng, device=self.device)
        * (high_thdot - low_thdot)
        + low_thdot
    )
    out = TensorDict(
        {
            "th": th,
            "thdot": thdot,
            "params": tensordict["params"],
        },
        batch_size=tensordict.shape,
    )
    return out


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