unilab.envs.mdp.curriculums.reward_curriculum

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

Bases: object

Update a reward term’s weight and/or params based on training steps.

Each stage specifies a step threshold and optionally a weight and/or params dict. When env.common_step_counter reaches a stage’s step, the corresponding values are applied. Later stages take precedence when multiple thresholds are reached.

Example owner YAML:

curriculum:
  action_rate_ramp:
    func: unilab.envs.mdp.reward_curriculum
    params:
      reward_name: action_rate
      stages:
        - {step: 0, weight: -0.1}
        - {step: 12000, weight: -0.4}
        - {step: 24000, weight: -1.0, params: {max_vel: 1.0}}
Parameters:

  • cfg (CurriculumTermCfg)

  • env (ManagerBasedRlEnv)

Methods

__init__(cfg, env)

__init__(cfg, env)[source]
Parameters:

  • cfg (CurriculumTermCfg)

  • env (ManagerBasedRlEnv)

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

Call self as a function.

Parameters:
Return type:

dict[str, Any]