Hierarchical Reinforcement Learning

Benchmarks archive 2025-07-28

1 leaderboard table shown for this task, 1 with rows (a “benchmark” on this site is a table with at least one row, as on /sota), ordered by row count. “Best model” is the first row in the archive's own order at snapshot; nothing is re-ranked here and metric direction is not recorded in the archive. PwC's Trend sparklines are not in the archive, so that column is omitted.

Syntology column: samples harvested from the paper's repositories and executed on synthesized fixtures; “ran” is not a correctness claim and does not order the table. A dash means no Syntology record for that paper, not a recorded non-run. Read from the graph 2026-09-24.

Libraries

Not in the archive: the export carries no per-task library table, so there is nothing to show at snapshot 2025-07-28.

Datasets archive 2025-07-28

2 datasets whose archive record lists this task, ordered by the archive's paper count.

Subtasks archive 2025-07-28

No subtask under this task in the archive's task tree.

Most implemented papers archive 2025-07-28

30 shown of 111 papers with code (384 tagged with this task in all), ordered by repositories listed in the archive, not by stars (the archive holds no stars, so PwC's “Social” and “Latest” sorts cannot be reproduced). Papers without a page here are shown as plain text.

• A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning
  12 Dec 2010 15 repositories listed Syntology ran 0 of 16 samples · 16 unverified
  We present a tutorial on Bayesian optimization, a method of finding the maximum of expensive cost functions.
• Data-Efficient Hierarchical Reinforcement Learning
  21 May 2018 12 repositories listed
  In this paper, we study how we can develop HRL algorithms that are general, in that they do not make onerous additional assumptions beyond standard RL algorithms, and efficient, in the sense that they can be used with…
• Near-Optimal Representation Learning for Hierarchical Reinforcement Learning
  2 Oct 2018 6 repositories listed
  We study the problem of representation learning in goal-conditioned hierarchical reinforcement learning.
• Hierarchical Reinforcement Learning with the MAXQ Value Function Decomposition
  21 May 1999 5 repositories listed
  The paper presents an online model-free learning algorithm, MAXQ-Q, and proves that it converges wih probability 1 to a kind of locally-optimal policy known as a recursively optimal policy, even in the presence of the…
• Hierarchical Reinforcement Learning for Automatic Disease Diagnosis
  29 Apr 2020 4 repositories listed
  Existing approaches usually employ a flat policy structure that treat all symptoms and diseases equally for action making.
• Learning Multi-Level Hierarchies with Hindsight
  4 Dec 2017 4 repositories listed
  Hierarchical agents have the potential to solve sequential decision making tasks with greater sample efficiency than their non-hierarchical counterparts because hierarchical agents can break down tasks into sets of…
• ReasonFlux: Hierarchical LLM Reasoning via Scaling Thought Templates
  10 Feb 2025 3 repositories listed
  We train our ReasonFlux-32B model with only 8 GPUs and introduces three innovations: (i) a structured and generic thought template library, containing around 500 high-level thought templates capable of generalizing to…
• Step by step: a hierarchical framework for multi-hop knowledge graph reasoning with reinforcement learning
  19 Jul 2022 3 repositories listed
  Due to this one-to-many dilemma, enlarged action space and ignoring logical relationship between entity and relation increase the difficulty of learning.
• HG2P: Hippocampus-inspired High-reward Graph and Model-Free Q-Gradient Penalty for Path Planning and Motion Control
  12 Oct 2024 2 repositories listed
  Goal-conditioned hierarchical reinforcement learning (HRL) decomposes complex reaching tasks into a sequence of simple subgoal-conditioned tasks, showing significant promise for addressing long-horizon planning in…
• Joint Admission Control and Resource Allocation of Virtual Network Embedding via Hierarchical Deep Reinforcement Learning
  25 Jun 2024 2 repositories listed Syntology ran 3 of 7 samples · 4 unverified · 7 pointer-only (licence)
  Specifically, the whole VNE process is decomposed into an upper-level policy for deciding whether to admit the arriving VNR or not and a lower-level policy for allocating resources of the physical network to meet the…
• Towards Automated Imbalanced Learning with Deep Hierarchical Reinforcement Learning
  26 Aug 2022 2 repositories listed
  Motivated by this, we investigate developing a learning-based over-sampling algorithm to optimize the classification performance, which is a challenging task because of the huge and hierarchical decision space.
• Online Baum-Welch algorithm for Hierarchical Imitation Learning
  22 Mar 2021 2 repositories listed
  This problem is referred to as hierarchical imitation learning and can be handled as an inference problem in a Hidden Markov Model, which is done via an Expectation-Maximization type algorithm.
• Modelling Hierarchical Structure between Dialogue Policy and Natural Language Generator with Option Framework for Task-oriented Dialogue System
  11 Jun 2020 2 repositories listed
  We test HDNO on MultiWoz 2.
• Combinatorial Optimization by Graph Pointer Networks and Hierarchical Reinforcement Learning
  12 Nov 2019 2 repositories listed Syntology ran 0 of 9 samples · 9 unverified
  Furthermore, to approximate solutions to constrained combinatorial optimization problems such as the TSP with time windows, we train hierarchical GPNs (HGPNs) using RL, which learns a hierarchical policy to find an…
• Learning Fairness in Multi-Agent Systems
  31 Oct 2019 2 repositories listed
  Fairness is essential for human society, contributing to stability and productivity.
• Learning Goal Embeddings via Self-Play for Hierarchical Reinforcement Learning
  22 Nov 2018 2 repositories listed
  In hierarchical reinforcement learning a major challenge is determining appropriate low-level policies.
• A Hierarchical Framework for Relation Extraction with Reinforcement Learning
  9 Nov 2018 2 repositories listed
  The whole extraction process is decomposed into a hierarchy of two-level RL policies for relation detection and entity extraction respectively, so that it is more feasible and natural to deal with overlapping relations.
• Stochastic Neural Networks for Hierarchical Reinforcement Learning
  10 Apr 2017 2 repositories listed
  Then a high-level policy is trained on top of these skills, providing a significant improvement of the exploration and allowing to tackle sparse rewards in the downstream tasks.
• SkillBlender: Towards Versatile Humanoid Whole-Body Loco-Manipulation via Skill Blending
  11 Jun 2025 1 repository listed
  To that end, we introduce SkillBlender, a novel hierarchical reinforcement learning framework for versatile humanoid loco-manipulation.
• From Debate to Equilibrium: Belief-Driven Multi-Agent LLM Reasoning via Bayesian Nash Equilibrium
  9 Jun 2025 1 repository listed Syntology ran 4 of 4 samples · 0 unverified
  To overcome these challenges, we recast multi-LLM coordination as an incomplete-information game and seek a Bayesian Nash equilibrium (BNE), in which each agent optimally responds to its probabilistic beliefs about the…
• Divide and Conquer: Grounding LLMs as Efficient Decision-Making Agents via Offline Hierarchical Reinforcement Learning
  26 May 2025 1 repository listed
  While showing sophisticated reasoning abilities, large language models (LLMs) still struggle with long-horizon decision-making tasks due to deficient exploration and long-term credit assignment, especially in…
• Implicitly Aligning Humans and Autonomous Agents through Shared Task Abstractions
  7 May 2025 1 repository listed Syntology ran 0 of 7 samples · 7 unverified
  In collaborative tasks, autonomous agents fall short of humans in their capability to quickly adapt to new and unfamiliar teammates.
• Solving Sokoban using Hierarchical Reinforcement Learning with Landmarks
  6 Apr 2025 1 repository listed
  We introduce a novel hierarchical reinforcement learning (HRL) framework that performs top-down recursive planning via learned subgoals, successfully applied to the complex combinatorial puzzle game Sokoban.
• Learning with Expert Abstractions for Efficient Multi-Task Continuous Control
  19 Mar 2025 1 repository listed
  Our empirical evaluation on a suite of procedurally generated continuous control environments demonstrates that our approach outperforms existing hierarchical reinforcement learning methods in terms of sample…
• Multi-Turn Code Generation Through Single-Step Rewards
  27 Feb 2025 1 repository listed
  We provide analysis of the design choices of the reward models and policy, and show the efficacy of μCode at utilizing the execution feedback.
• TAG: A Decentralized Framework for Multi-Agent Hierarchical Reinforcement Learning
  21 Feb 2025 1 repository listed
  Hierarchical organization is fundamental to biological systems and human societies, yet artificial intelligence systems often rely on monolithic architectures that limit adaptability and scalability.
• Autonomous Option Invention for Continual Hierarchical Reinforcement Learning and Planning
  20 Dec 2024 1 repository listed
  Our approach continually learns and maintains an interpretable state abstraction, and uses it to invent high-level options with abstract symbolic representations.
• Meta-Learning Integration in Hierarchical Reinforcement Learning for Advanced Task Complexity
  10 Oct 2024 1 repository listed
  To address this, we integrate meta-learning into HRL to enhance the agent's ability to learn and adapt hierarchical policies swiftly.
• Selective Exploration and Information Gathering in Search and Rescue Using Hierarchical Learning Guided by Natural Language Input
  20 Sep 2024 1 repository listed
  In recent years, robots and autonomous systems have become increasingly integral to our daily lives, offering solutions to complex problems across various domains.
• Item-Difficulty-Aware Learning Path Recommendation: From a Real Walking Perspective
  24 Aug 2024 1 repository listed
  Learning path recommendation aims to provide learners with a reasonable order of items to achieve their learning goals.

Syntology lines on 5 of the papers shown; no Syntology record for the others (a paper without an arXiv id cannot be joined to the graph, and absence from the graph layer is not a recorded non-run). “Ran” means the sample executed on a synthesized fixture, not that the paper's result was reproduced. Read from the graph 2026-09-24.