unilab.envs.mdp.curriculums.command_curriculum

class unilab.envs.mdp.curriculums.command_curriculum[source]

Bases: object

Update a command term’s config fields and/or params based on training steps.

Command terms read their live self.cfg at resample time, so mutating the resolved term config (e.g. rel_standing_envs or ranges) takes effect on the next command resample. Stage semantics match reward_curriculum: every stage whose step has been reached is applied in order, so later stages win.

Example owner YAML:

curriculum:
  standing_envs:
    func: unilab.envs.mdp.command_curriculum
    params:
      command_name: twist
      stages:
        - {step: 0, rel_standing_envs: 0.02}
        - {step: 12000, rel_standing_envs: 0.1}
Parameters:

  • cfg (CurriculumTermCfg)

  • env (ManagerBasedRlEnv)

Methods

__init__(cfg, env)

__init__(cfg, env)[source]
Parameters:

  • cfg (CurriculumTermCfg)

  • env (ManagerBasedRlEnv)

__call__(env, env_ids, command_name, stages)[source]

Call self as a function.

Parameters:
Return type:

dict[str, Any]