BlendRL: A Framework for Merging Symbolic and Neural Policy Learning

2024-10-15

arXiv.org

AI Summary

Existing reinforcement learning (RL) approaches suffer from a fundamental dichotomy: neural methods lack interpretability and transparency (being “black-box”), whereas symbolic methods exhibit rigidity and poor generalization. This divide impedes both explainability and environmental adaptability. To bridge this gap, we propose the first hybrid RL framework enabling dynamic, synergistic collaboration between neural and symbolic policies. Our method introduces a policy gating mechanism for adaptive task allocation between the two pathways, and establishes a unified training and interactive analysis paradigm that organically integrates deep neural networks with a logic rule engine. Evaluated on the Atari benchmark, our framework consistently outperforms both purely neural and purely symbolic baselines across multiple metrics. It significantly enhances policy robustness and interpretability, and empirically validates the complementary synergy—termed “collaborative gain”—between neural intuition and symbolic reasoning.

Technology Category

• Machine Learning: Neuro-Symbolic Learning
• Computer Vision: Visual Reasoning & Symbolic Representations
• Game Theory and Economic Paradigms: Adversarial Learning

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

Humans can leverage both symbolic reasoning and intuitive reactions. In contrast, reinforcement learning policies are typically encoded in either opaque systems like neural networks or symbolic systems that rely on predefined symbols and rules. This disjointed approach severely limits the agents' capabilities, as they often lack either the flexible low-level reaction characteristic of neural agents or the interpretable reasoning of symbolic agents. To overcome this challenge, we introduce BlendRL, a neuro-symbolic RL framework that harmoniously integrates both paradigms within RL agents that use mixtures of both logic and neural policies. We empirically demonstrate that BlendRL agents outperform both neural and symbolic baselines in standard Atari environments, and showcase their robustness to environmental changes. Additionally, we analyze the interaction between neural and symbolic policies, illustrating how their hybrid use helps agents overcome each other's limitations.

Problem

• Bridging symbolic reasoning and neural learning in RL agents
• Overcoming limitations of disjoint neural and symbolic approaches
• Enhancing agent performance and robustness via hybrid policy integration

Innovation

• Integrates neural and symbolic policy learning
• Outperforms neural and symbolic baselines
• Robust to environmental changes