Neurosymbolic Reasoning with Incremental Knowledge for Sample Efficient Hierarchical Reinforcement Learning
Quick Answer
This paper introduces neurosymbolic Hierarchical Reinforcement Learning (HRL) with Incremental Knowledge (InK), enhancing sample efficiency in sparse reward environments.
Quick Take
By integrating symbolic planning and goal-conditioned neural modules, the proposed method significantly improves navigation task performance, demonstrating the effectiveness of updatable knowledge representations.
Key Points
- Neurosymbolic HRL integrates symbolic planning with updatable knowledge representations.
- Incremental Knowledge (InK) enhances sample efficiency in long-horizon reasoning tasks.
- Experiments show significant performance improvements in navigation tasks.
- Belief World Tree Search enables optimal symbolic planning with prior knowledge.
- Code for the proposed method is publicly available.
DeepSignal Analysis
What happened
The paper presents a novel approach to Hierarchical Reinforcement Learning (HRL) that incorporates Incremental Knowledge (InK) to enhance sample efficiency in environments with sparse rewards. By combining symbolic planning with neural modules, the method shows improved performance in navigation tasks, indicating a significant advancement in the field.
Key evidence
- The proposed neurosymbolic HRL framework integrates symbolic planning and goal-conditioned neural modules to address challenges in sparse reward environments.
- In traditional HRL, knowledge is fixed and non-updatable, which limits the agent's ability to reason effectively during exploration.
- Experiments demonstrate that the incorporation of InK leads to substantial improvements in sample efficiency for navigation tasks.
Why it matters
This research addresses a critical limitation in traditional reinforcement learning methods, particularly in environments where rewards are sparse and long-term reasoning is required. By enabling agents to update their knowledge representations dynamically, the approach could lead to more efficient learning and decision-making processes, potentially impacting various applications in AI and robotics.
Paper Resources
📖 Reader Mode
~2 min readAbstract:(Flat) Reinforcement Learning (RL) agents face significant challenges in environments with sparse rewards that require long-horizon reasoning. A compelling approach to improve sample efficiency is to incorporate knowledge into learning and decision-making. In standard Hierarchical RL (HRL), knowledge is encoded in a fixed, non-updatable form, such as architectural choices, and remains unchanged throughout learning. With fixed HRL, reasoning with incremental knowledge learned during exploration is impractical before sufficient environmental knowledge is acquired, leading to poor sample efficiency. In this work, we propose neurosymbolic HRL with {\em Incremental Knowledge (InK)}: symbolic high-level components perform {\em symbolic planning} (e.g. using $D^*$) on an updatable representation of current InK, while low-level goal-conditioned neural modules learn motion primitives through experience using reward shaping. Experiments on navigation tasks demonstrate that incorporating InK substantially improves sample efficiency. Additionally, to perform {\em optimal} symbolic planning given {\em prior} knowledge about the world, we develop Belief World Tree Search. The code is available at this https URL.
| Comments: | Published in ECML-PKDD 2026 |
| Subjects: | Artificial Intelligence (cs.AI); Robotics (cs.RO) |
| Cite as: | arXiv:2608.02993 [cs.AI] |
| (or arXiv:2608.02993v1 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2608.02993 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Subrat Panda [view email]
[v1]
Tue, 4 Aug 2026 01:17:39 UTC (1,657 KB)
— Originally published at arxiv.org
Want this in your inbox every morning?
Daily brief at your local 8am — bilingual EN/中文, free.
More from arXiv cs.AI
See more →HOBA: Hierarchical On-Policy Bidding Agents for Adaptive Online Advertising
HOBA (Hierarchical On-policy Bidding Agents) is a novel hierarchical reinforcement learning framework that enhances online advertising bidding systems by improving adaptability and reducing hyperparameter tuning costs. It utilizes a for hyperparameter inference, a SARSA agent for expert model selection, and a dynamic expert pool for bid execution, achieving a +3.6% increase in target cost during large-scale deployment and outperforming state-of-the-art baselines on AuctionNet.