New AI Research Proposes Multi-Resolution Skills to Improve Hierarchical Reinforcement Learning
A recent study presents Multi-Resolution Skills (MRS), a novel approach aimed at overcoming challenges in hierarchical reinforcement learning (HRL). While HRL breaks down policies into manager and worker roles for extended planning, it often struggles with tasks that demand agility. The study pinpoints a fundamental issue: in subgoal-based HRL, the manager's goal representation frequently lacks constraints regarding reachability or the temporal gap from current states, hindering accurate local subgoal selection. The researchers reveal that the ideal subgoal distance is influenced by both the task and state—proximity allows for precise control but increases prediction noise, whereas distant subgoals yield smoother motion at the expense of geometric accuracy. MRS develops various goal-prediction modules tailored to specific temporal horizons, supported by a collaboratively trained meta-controller that chooses among them based on the current state. This method consistently surpasses fixed-resolution benchmarks. The findings were shared on arXiv under identifier arXiv:2505.21410v2.
Key facts
- The paper introduces Multi-Resolution Skills (MRS) for hierarchical reinforcement learning
- HRL decomposes policy into manager and worker components for long-horizon planning
- HRL suffers performance gaps on tasks requiring agility
- In subgoal-based HRL, manager's goal representation typically lacks constraints on reachability or temporal distance
- Optimal subgoal distance is both task- and state-dependent
- Nearby subgoals enable precise control but amplify prediction noise
- Distant subgoals produce smoother motion at the cost of geometric precision
- MRS learns multiple goal-prediction modules specialized to fixed temporal horizons
Entities
Institutions
- arXiv