MRSD: Multi-Resolution Skill Discovery for HRL Agents

Hierarchical reinforcement learning (HRL) relies on abstract skills to solve long-horizon tasks efficiently. While existing skill discovery methods learns these skills automatically, they are limited to a single skill per task. In contrast, humans learn and use both fine-grained and coarse motor skills simultaneously. Inspired by human motor control, we propose Multi-Resolution Skill Discovery (MRSD), an HRL framework that learns multiple skill encoders at different temporal resolutions in parallel. A high-level manager dynamically selects among these skills, enabling adaptive control strategies over time. We evaluate MRSD on tasks from the DeepMind Control Suite and show that it outperforms prior state-of-the-art skill discovery and HRL methods, achieving faster convergence and higher final performance. Our findings highlight the benefits of integrating multi-resolution skills in HRL, paving the way for more versatile and efficient agents.

DHP: Discrete Hierarchical Planning for Hierarchical Reinforcement Learning Agents

In this paper, we address the challenge of long-horizon visual planning tasks using Hierarchical Reinforcement Learning (HRL). Our key contribution is a Discrete Hierarchical Planning (DHP) method, an alternative to traditional distance-based approaches. We provide theoretical foundations for the method and demonstrate its effectiveness through extensive empirical evaluations. Our agent recursively predicts subgoals in the context of a long-term goal and receives discrete rewards for constructing plans as compositions of abstract actions. The method introduces a novel advantage estimation strategy for tree trajectories, which inherently encourages shorter plans and enables generalization beyond the maximum tree depth. The learned policy function allows the agent to plan efficiently, requiring only $\log N$ computational steps, making re-planning highly efficient. The agent, based on a soft-actor critic (SAC) framework, is trained using on-policy imagination data. Additionally, we propose a novel exploration strategy that enables the agent to generate relevant training examples for the planning modules. We evaluate our method on long-horizon visual planning tasks in a 25-room environment, where it significantly outperforms previous benchmarks at success rate and average episode length. Furthermore, an ablation study highlights the individual contributions of key modules to the overall performance.