Boosting deep Reinforcement Learning using pretraining with Logical Options
arXiv:2603.06565v1 Announce Type: new Abstract: Deep reinforcement learning agents are often misaligned, as they over-exploit early reward signals. Recently, several symbolic approaches have addressed these challenges by encoding sparse objectives along with aligned plans. However, purely symbolic architectures are complex to scale and difficult to apply to continuous settings. Hence, we propose a hybrid approach, inspired by humans' ability to acquire new skills. We use a two-stage framework that injects symbolic structure into neural-based reinforcement learning agents without sacrificing the expressivity of deep policies. Our method, called Hybrid Hierarchical RL (H^2RL), introduces a logical option-based pretraining strategy to steer the learning policy away from short-term reward loops and toward goal-directed behavior while allowing the final policy to be refined via standard environment interaction. Empirically, we show that this approach consistently improves long-horizon deci
arXiv:2603.06565v1 Announce Type: new Abstract: Deep reinforcement learning agents are often misaligned, as they over-exploit early reward signals. Recently, several symbolic approaches have addressed these challenges by encoding sparse objectives along with aligned plans. However, purely symbolic architectures are complex to scale and difficult to apply to continuous settings. Hence, we propose a hybrid approach, inspired by humans' ability to acquire new skills. We use a two-stage framework that injects symbolic structure into neural-based reinforcement learning agents without sacrificing the expressivity of deep policies. Our method, called Hybrid Hierarchical RL (H^2RL), introduces a logical option-based pretraining strategy to steer the learning policy away from short-term reward loops and toward goal-directed behavior while allowing the final policy to be refined via standard environment interaction. Empirically, we show that this approach consistently improves long-horizon decision-making and yields agents that outperform strong neural, symbolic, and neuro-symbolic baselines.
Executive Summary
The article introduces Hybrid Hierarchical RL (H^2RL), a novel hybrid framework that integrates logical options into deep reinforcement learning to mitigate misalignment caused by premature exploitation of reward signals. By leveraging a two-stage pretraining strategy that injects symbolic structure without compromising the expressive capacity of deep policies, H^2RL aims to redirect agent behavior toward goal-directed outcomes. Empirical results demonstrate consistent improvements in long-horizon decision-making, with outperformance of both neural and neuro-symbolic baselines. The approach effectively bridges the gap between scalable deep learning and structured symbolic reasoning.
Key Points
- ▸ Introduction of a hybrid framework combining symbolic pretraining with neural RL to address misalignment.
- ▸ Use of logical options as a pretraining mechanism to redirect agent focus from short-term to long-term goals.
- ▸ Empirical validation showing improved performance against diverse baselines.
Merits
Innovative Integration
H^2RL successfully merges symbolic and neural paradigms without sacrificing policy expressiveness, offering a balanced solution for complex RL environments.
Empirical Validation
Strong empirical results validate the effectiveness of the hybrid approach in improving long-horizon performance.
Demerits
Scalability Concerns
While effective, the hybrid architecture may introduce additional complexity in hyperparameter tuning and integration, potentially complicating deployment in large-scale or real-time systems.
Expert Commentary
The paper presents a compelling and well-structured contribution to the field of deep reinforcement learning. The hybrid model architecture is particularly noteworthy for its elegance in combining symbolic pretraining with deep policy learning, avoiding the pitfalls of pure symbolic or pure neural approaches. The logical option-based pretraining strategy is a creative mechanism that aligns well with human-inspired skill acquisition. Moreover, the empirical validation against diverse baselines strengthens the credibility of the claims. While the authors acknowledge the potential for increased complexity, their analysis of trade-offs is pragmatic and realistic. This work represents a meaningful step forward in the evolution of hybrid AI systems, particularly for applications requiring both efficiency and interpretability.
Recommendations
- ✓ Researchers should extend H^2RL to explore application domains beyond the current evaluation scope, such as multi-agent environments or real-time robotics.
- ✓ Industry practitioners should consider pilot implementations in high-stakes RL settings where long-horizon planning is critical, such as autonomous vehicle navigation or financial trading.