Reinforcement Learning with Anticipation: A Hierarchical Approach for Long-Horizon Tasks

• URL: http://arxiv.org/abs/2509.05545v1
• Date: Sat, 06 Sep 2025 00:10:15 GMT
• Title: Reinforcement Learning with Anticipation: A Hierarchical Approach for Long-Horizon Tasks
• Authors: Yang Yu,
• Abstract summary: Solving long-horizon goal-conditioned tasks remains a significant challenge in reinforcement learning.<n>We introduce Reinforcement Learning with Anticipation (RLA), a principled and potentially scalable framework designed to address these limitations.<n>Key feature of RLA is the training of the anticipation model, which is guided by a principle of value geometric consistency.
• Score: 3.79187263097166
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Solving long-horizon goal-conditioned tasks remains a significant challenge in reinforcement learning (RL). Hierarchical reinforcement learning (HRL) addresses this by decomposing tasks into more manageable sub-tasks, but the automatic discovery of the hierarchy and the joint training of multi-level policies often suffer from instability and can lack theoretical guarantees. In this paper, we introduce Reinforcement Learning with Anticipation (RLA), a principled and potentially scalable framework designed to address these limitations. The RLA agent learns two synergistic models: a low-level, goal-conditioned policy that learns to reach specified subgoals, and a high-level anticipation model that functions as a planner, proposing intermediate subgoals on the optimal path to a final goal. The key feature of RLA is the training of the anticipation model, which is guided by a principle of value geometric consistency, regularized to prevent degenerate solutions. We present proofs that RLA approaches the globally optimal policy under various conditions, establishing a principled and convergent method for hierarchical planning and execution in long-horizon goal-conditioned tasks.

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