Boosting deep Reinforcement Learning using pretraining with Logical Options

RSCT Certification: κ=0.549 (pending) | RSN: 0.37/0.32/0.31 | Topics: llm-agents

Boosting deep Reinforcement Learning using pretraining with Logical Options: An RSCT Analysis

Core Contribution The paper addresses a key challenge in deep reinforcement learning (RL) - the tendency of agents to over-exploit early reward signals, leading to misaligned behavior. To overcome this, the authors propose a hybrid approach called Hybrid Hierarchical RL (H^2RL) that injects symbolic structure into neural-based RL agents without sacrificing the expressivity of deep policies.

The key innovation is a two-stage framework that uses a logical option-based pretraining strategy to steer the learning policy away from short-term reward loops and toward goal-directed behavior. This allows the final policy to be refined via standard environment interaction, leveraging the advantages of both symbolic and neural techniques.

Technical Approach H^2RL consists of two main components: a logical option-based pretraining stage and a final policy refinement stage. In the pretraining stage, the agent learns a set of high-level logical options, which encode symbolic knowledge about the task structure and desired behavior. These options act as macro-actions, guiding the agent toward more purposeful exploration and long-term planning.

The pretraining stage involves learning a policy over the logical options using a symbolic planner. This policy is then used to initialize the agent's deep neural network policy, which is further refined through standard RL interaction with the environment. The authors show that this hybrid approach allows the agent to benefit from the structured exploration afforded by the logical options, while retaining the flexibility and expressivity of deep RL policies.

Key Results The authors evaluate H^2RL on a set of challenging continuous control tasks, including long-horizon navigation and object manipulation problems. They show that H^2RL consistently outperforms strong neural, symbolic, and neuro-symbolic baselines, particularly in terms of long-horizon decision-making and goal-directed behavior.

Specifically, H^2RL agents demonstrate more efficient exploration, better credit assignment, and more stable and reliable performance compared to the baselines. The authors provide comprehensive experimental results, including comparisons to state-of-the-art methods across various metrics and benchmarks.

Significance & Limitations The paper's significance lies in its ability to address a fundamental challenge in deep RL - the tendency of agents to get stuck in local optima due to myopic reward-seeking behavior. By incorporating symbolic knowledge through logical options, H^2RL enables agents to learn more purposeful and goal-directed policies, which is crucial for deployment in real-world applications.

However, the paper also acknowledges some limitations. The approach requires the designer to specify the logical options, which may not always be feasible or straightforward, especially for complex tasks. Additionally, the pretraining stage adds computational overhead, which may limit the scalability of the method to very large-scale problems.

Through the RSCT Lens H^2RL's approach aligns well with several key principles of Representation-Space Compatibility Theory (RSCT). By injecting symbolic knowledge through logical options, the method directly addresses the issue of representation quality (R), as the options help the agent better represent the underlying task structure and desired behavior.

Moreover, the two-stage framework, with pretraining followed by policy refinement, enhances the stability (S) of the learned policies. The logical options provide a stable, goal-directed foundation, while the final RL stage allows for further refinement and adaptation to the specific environment dynamics.

The paper's κ-gate score of 0.549 suggests that the contributions are reasonably well-integrated with existing knowledge, but further work may be needed to fully optimize the representation-space compatibility. The relatively balanced R, S, and N scores (0.37, 0.32, 0.31, respectively) indicate that the paper provides a solid technical contribution, but there may be some room for improvement in reducing noise (N) and further boosting the relevance (R) and stability (S) of the findings.

To further improve the RSCT score, the authors could explore ways to make the logical option specification more automated or data-driven, reducing the reliance on manual designer input. Additionally, investigating the robustness and generalization of the learned policies across a wider range of environments and tasks could strengthen the stability (S) component. By addressing these aspects, the paper's compatibility with existing knowledge and its overall RSCT certification could be enhanced.

Paper Details

• Authors: Zihan Ye, Phil Chau, Raban Emunds, Jannis Blüml, Cedric Derstroff
• Source: arXiv
• PDF: Download
• Published: 2026-03-06

This analysis was generated by the Swarm-It RSCT pipeline using Claude.