Publikation
BlendRL: A Framework for Merging Symbolic and Neural Policy Learning
Hikaru Shindo; Quentin Delfosse; Devendra Singh Dhami; Kristian Kersting
In: Computing Research Repository eprint Journal (CoRR), Vol. abs/2410.11689, Pages 1-32, arXiv, 2024.
Zusammenfassung
Humans can leverage both abstract reasoning and intuitive reactions. In contrast, rein-
forcement 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 frame-
work that harmoniously integrates both paradigms within RL agents that use mixtures of
both logic and neural policies. We empirically demonstrate that BlendRL agents outper-
form both neural and symbolic baselines in standard Atari environments, and showcase
their robustness to environmental changes. Additionally, we analyze the interaction be-
tween neural and symbolic policies, illustrating how their hybrid use helps agents over-
come each other’s limitations.