Actoragnostic

Actoragnostic is a term used in reinforcement learning and related areas to describe approaches, models, or evaluations that do not depend on a specific actor policy or its parameterization. The idea is to separate the learning of value estimates, representations, or decision-making logic from the details of the actor, enabling reuse and transfer across different policies.

In practice, actor-agnostic components may include critics or feature extractors that are trained to estimate value functions or environment dynamics without being tied to a particular actor's action space. Such components can be paired with various actors, allowing experimentation with different policy architectures without retraining shared components. This can facilitate transfer learning, multi-task learning, or evaluation of policy performance independent of actor design. The concept is common in discussions of actor-critic methods, where the critic is ideally agnostic to the actor.

The terminology is not universally standardized, and writers may use "actor-agnostic," "policy-agnostic," or "actor-independent" to describe related ideas. Potential benefits include modularity, robustness, and faster experimentation, but challenges include ensuring stability across different actors and avoiding hidden biases toward certain policy classes. See also reinforcement learning, actor-critic methods, value function approximation.