Sample-Efficient Neurosymbolic Deep Reinforcement Learning
Summary: arXiv:2601.02850v2 Announce Type: replace
Abstract: Reinforcement Learning (RL) is a well-established framework for sequential decision-making in complex environments. However, state-of-the-art Deep RL (DRL) algorithms typically require large training datasets and often struggle to generalize beyond small-scale training scenarios, even within standard benchmarks.
In this article, we present a novel neuro-symbolic Deep Reinforcement Learning approach that integrates background symbolic knowledge to enhance sample efficiency and generalization capabilities for unseen tasks. This approach aims to address the limitations of conventional DRL algorithms, which can be data-hungry and may not perform well outside of their training environments.
Key Innovations in Neuro-Symbolic DRL
The proposed methodology incorporates the following innovative features:
- Transfer of Partial Policies: The method utilizes partial policies defined for simpler domain instances, where achieving high performance is feasible. These policies serve as valuable priors that accelerate learning in more complex environments, thus avoiding the need to tune DRL parameters from scratch.
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Logical Rule Representation: Partial policies are represented as logical rules, which facilitate online reasoning. This representation supports the training process in two key ways:
- Biasing Action Distribution: During exploration, the action distribution is biased to favor actions suggested by the logical rules.
- Rescaling Q-values: During exploitation, Q-values are adjusted based on insights from the symbolic knowledge, enhancing decision-making quality.
- Enhanced Interpretability and Trustworthiness: By employing a neuro-symbolic framework, the approach improves the interpretability of the learned policies, fostering a greater degree of trust in the decisions made by the algorithm.
- Accelerated Convergence: The integration of symbolic reasoning significantly speeds up the convergence of the learning process, particularly in environments characterized by sparse rewards and tasks with extended planning horizons.
Empirical Validation
To validate the effectiveness of our neuro-symbolic DRL approach, we conducted extensive experiments in various challenging gridworld environments. These experiments included both fully observable and partially observable settings, allowing us to rigorously assess the performance enhancements offered by our methodology.
Our results demonstrate a marked improvement in performance compared to a state-of-the-art reward machine baseline. The findings indicate that the proposed neuro-symbolic integration not only enhances sample efficiency but also improves the ability of the DRL algorithms to generalize across different tasks.
Conclusion
This research highlights the potential of integrating symbolic knowledge into deep reinforcement learning frameworks to address key challenges in the field. As the demand for more robust and efficient AI systems grows, our neuro-symbolic approach may pave the way for advancements in RL applications across various complex environments.