lépésrl

lépésrl is a theoretical framework and software concept used in discussions of stepwise decision making in artificial intelligence and education technology. It promotes decomposing complex tasks into discrete steps and coupling each step with reinforcement signals to guide learning and behavior.

The name blends the Hungarian word lépés, meaning step, with RL, a common abbreviation for reinforcement learning. In academic and practical discussions, lépésrl is presented as a framework for structuring action sequences as manageable units.

Central components include a step planner that defines an ordered sequence of actions, a policy evaluator that measures outcomes at each step, and an explainable user interface that presents the rationale for decisions. The approach emphasizes traceability, modularity, and incremental improvement.

lépésrl arose in speculative AI literature and experimental education tools during the 2020s as a stepwise variant of reinforcement learning designed for human-in-the-loop tasks. It has not become a widely adopted standard and remains primarily a topic of research and experimentation.

Potential applications include educational simulators and tutoring systems that scaffold tasks, as well as transparent automation tools in industry. Critics note potential rigidity, overhead from step-level auditing, and the challenge of aligning steps with diverse user goals and contexts.

See also: reinforcement learning, step-based planning, explainable AI.