unilab.algos.torch.hora.rsl_rl.HoraRslRlVecEnvWrapper

class unilab.algos.torch.hora.rsl_rl.HoraRslRlVecEnvWrapper[source]

Bases: RslRlVecEnvWrapper

RSL-RL adapter that preserves HORA teacher-policy observation payloads.

Parameters:

  • env (Any)

  • device (str)

  • policy_obs_mode (str)

Methods

__init__(env[, device, policy_obs_mode])

close()

get_observations()

Return the current HORA-aware observation TensorDict.

get_privileged_observations()

reset()

Reset the wrapped env and preserve HORA privileged reset payloads.

step(actions)

Step the wrapped env while keeping HORA bootstrap payloads intact.
step(actions)[source]

Step the wrapped env while keeping HORA bootstrap payloads intact.

Parameters:

actions (Tensor | ndarray) – Torch or numpy action batch with shape (num_envs, action_dim).

Return type:

tuple[TensorDict, Tensor, Tensor, dict]

Returns:

Tuple (obs_td, rewards, dones, infos) matching the RSL-RL VecEnv contract while preserving HORA privileged observations.

reset()[source]

Reset the wrapped env and preserve HORA privileged reset payloads.

Parameters:

None.

Return type:

tuple[TensorDict, dict[str, Any]]

Returns:

Tuple (obs_td, info) where obs_td retains HORA privileged inputs.

get_observations()[source]

Return the current HORA-aware observation TensorDict.

Parameters:

None.

Return type:

TensorDict

Returns:

TensorDict containing the current observation batch with HORA extras.

__init__(env, device='cpu', policy_obs_mode='flat')
Parameters:

  • env (Any)

  • device (str)

  • policy_obs_mode (str)

close()
Return type:

None

get_privileged_observations()
Return type:

Tensor