ClipTransform

class torchrl.envs.transforms.ClipTransform(in_keys=None, out_keys=None, in_keys_inv=None, out_keys_inv=None, *, low=None, high=None)

A transform to clip input (state, action) or output (observation, reward) values.

This transform can take multiple input or output keys but only one value per transform. If multiple clipping values are needed, several transforms should be appended one after the other.

Parameters:

• in_keys (list of NestedKeys) – input entries (read)

• out_keys (list of NestedKeys) – input entries (write)

• in_keys_inv (list of NestedKeys) – input entries (read) during inv() calls.

• out_keys_inv (list of NestedKeys) – input entries (write) during inv() calls.

Keyword Arguments:

• low (scalar, optional) – the lower bound of the clipped space.

• high (scalar, optional) – the higher bound of the clipped space.

Note

Providing just one of the arguments low or high is permitted, but at least one must be provided.

Examples

>>> from torchrl.envs.libs.gym import GymEnv
>>> base_env = GymEnv("Pendulum-v1")
>>> env = TransformedEnv(base_env, ClipTransform(in_keys=['observation'], low=-1, high=0.1))
>>> r = env.rollout(100)
>>> assert (r["observation"] <= 0.1).all()

transform_observation_spec(observation_spec: TensorSpec) → TensorSpec

Transforms the observation spec such that the resulting spec matches transform mapping.

Parameters:

observation_spec (TensorSpec) – spec before the transform

Returns:

expected spec after the transform

transform_reward_spec(reward_spec: TensorSpec) → TensorSpec

Transforms the reward spec such that the resulting spec matches transform mapping.

Parameters:

reward_spec (TensorSpec) – spec before the transform

Returns:

expected spec after the transform