BlendRL: A Framework for Merging Symbolic and Neural Policy Learning
Hikaru Shindo Quentin Delfosse Devendra Singh Dhami Kristian Kersting
Humans can leverage both symbolic reasoning and intuitive responses. 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. 
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.
Neuro-Symbolic AI, Differentiable Reasoning, Reinforcement Learning, Interpretable AI, First-order logic
We propose a framework that jointly learns symbolic and neural policies for reinforcement learning.