AutoResetTransform¶

class torchrl.envs.transforms.AutoResetTransform(*, replace: Optional[bool] = None, fill_float='nan', fill_int=- 1, fill_bool=False)[source]¶

A transform for auto-resetting environments.

This transform can be appended to any auto-resetting environment, or automatically appended using env = SomeEnvClass(..., auto_reset=True). If the transform is explicitly appended to an env, a AutoResetEnv must be used.

An auto-reset environment must have the following properties (differences from this description should be accounted for by subclassing this class):

the reset function can be called once at the beginning (after instantiation) with or without effect. Whether calls to reset are allowed after that is up to the environment itself.

During a rollout, any done state will result in a reset and produce an observation that isn’t the last observation of the current episode, but the first observation of the next episode (this transform will extract and cache this observation and fill the obs with some arbitrary value).

Keyword Arguments:

replace (bool, optional) – if False, values are just placed as they are in the "next" entry even if they are not valid. Defaults to True. A value of False overrides any subsequent filling keyword argument. This argumet can also be passed with the constructor method by passing a auto_reset_replace argument: env = FooEnv(..., auto_reset=True, auto_reset_replace=False).
fill_float (float or str, optional) – The filling value for floating point tensors that terminate an episode. A value of None means no replacement (values are just placed as they are in the "next" entry even if they are not valid).
fill_int (int, optional) – The filling value for signed integer tensors that terminate an episode. A value of None means no replacement (values are just placed as they are in the "next" entry even if they are not valid).
fill_bool (bool, optional) – The filling value for boolean tensors that terminate an episode. A value of None means no replacement (values are just placed as they are in the "next" entry even if they are not valid).

Arguments are only available when the transform is explicitly instantiated (not through EnvType(…, auto_reset=True)).

Examples

>>> from torchrl.envs import GymEnv
>>> from torchrl.envs import set_gym_backend
>>> import torch
>>> torch.manual_seed(0)
>>>
>>> class AutoResettingGymEnv(GymEnv):
...     def _step(self, tensordict):
...         tensordict = super()._step(tensordict)
...         if tensordict["done"].any():
...             td_reset = super().reset()
...             tensordict.update(td_reset.exclude(*self.done_keys))
...         return tensordict
...
...     def _reset(self, tensordict=None):
...         if tensordict is not None and "_reset" in tensordict:
...             return tensordict.copy()
...         return super()._reset(tensordict)
>>>
>>> with set_gym_backend("gym"):
...     env = AutoResettingGymEnv("CartPole-v1", auto_reset=True, auto_reset_replace=True)
...     env.set_seed(0)
...     r = env.rollout(30, break_when_any_done=False)
>>> print(r["next", "done"].squeeze())
tensor([False, False, False, False, False, False, False, False, False, False,
        False, False, False,  True, False, False, False, False, False, False,
        False, False, False, False, False,  True, False, False, False, False])
>>> print("observation after reset are set as nan", r["next", "observation"])
observation after reset are set as nan tensor([[-4.3633e-02, -1.4877e-01,  1.2849e-02,  2.7584e-01],
        [-4.6609e-02,  4.6166e-02,  1.8366e-02, -1.2761e-02],
        [-4.5685e-02,  2.4102e-01,  1.8111e-02, -2.9959e-01],
        [-4.0865e-02,  4.5644e-02,  1.2119e-02, -1.2542e-03],
        [-3.9952e-02,  2.4059e-01,  1.2094e-02, -2.9009e-01],
        [-3.5140e-02,  4.3554e-01,  6.2920e-03, -5.7893e-01],
        [-2.6429e-02,  6.3057e-01, -5.2867e-03, -8.6963e-01],
        [-1.3818e-02,  8.2576e-01, -2.2679e-02, -1.1640e+00],
        [ 2.6972e-03,  1.0212e+00, -4.5959e-02, -1.4637e+00],
        [ 2.3121e-02,  1.2168e+00, -7.5232e-02, -1.7704e+00],
        [ 4.7457e-02,  1.4127e+00, -1.1064e-01, -2.0854e+00],
        [ 7.5712e-02,  1.2189e+00, -1.5235e-01, -1.8289e+00],
        [ 1.0009e-01,  1.0257e+00, -1.8893e-01, -1.5872e+00],
        [        nan,         nan,         nan,         nan],
        [-3.9405e-02, -1.7766e-01, -1.0403e-02,  3.0626e-01],
        [-4.2959e-02, -3.7263e-01, -4.2775e-03,  5.9564e-01],
        [-5.0411e-02, -5.6769e-01,  7.6354e-03,  8.8698e-01],
        [-6.1765e-02, -7.6292e-01,  2.5375e-02,  1.1820e+00],
        [-7.7023e-02, -9.5836e-01,  4.9016e-02,  1.4826e+00],
        [-9.6191e-02, -7.6387e-01,  7.8667e-02,  1.2056e+00],
        [-1.1147e-01, -9.5991e-01,  1.0278e-01,  1.5219e+00],
        [-1.3067e-01, -7.6617e-01,  1.3322e-01,  1.2629e+00],
        [-1.4599e-01, -5.7298e-01,  1.5848e-01,  1.0148e+00],
        [-1.5745e-01, -7.6982e-01,  1.7877e-01,  1.3527e+00],
        [-1.7285e-01, -9.6668e-01,  2.0583e-01,  1.6956e+00],
        [        nan,         nan,         nan,         nan],
        [-4.3962e-02,  1.9845e-01, -4.5015e-02, -2.5903e-01],
        [-3.9993e-02,  3.9418e-01, -5.0196e-02, -5.6557e-01],
        [-3.2109e-02,  5.8997e-01, -6.1507e-02, -8.7363e-01],
        [-2.0310e-02,  3.9574e-01, -7.8980e-02, -6.0090e-01]])

forward(tensordict: TensorDictBase) → TensorDictBase[source]¶: Reads the input tensordict, and for the selected keys, applies the transform.

AutoResetTransform¶

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