Learning for long-horizon planning via neuro-symbolic abductive imitation

Analysis

Why This Paper Matters

Long-horizon planning remains a critical challenge in reinforcement learning, where agents must reason over extended sequences of actions. Purely neural methods often struggle with credit assignment and sample efficiency, while symbolic planners require hand-crafted rules. This paper proposes a neuro-symbolic approach that leverages the strengths of both paradigms: neural networks for perception and control, and symbolic reasoning for structured planning. By introducing abductive imitation learning, the method can infer missing subgoals from demonstrations, making it particularly relevant for tasks where full expert trajectories are unavailable.

Technical Contributions

The key innovations include:

• Neuro-symbolic architecture: A neural policy is augmented with a symbolic planner that reasons over abstract state representations.
• Abductive imitation learning: The system uses abduction to infer latent subgoals that explain observed behavior, enabling learning from partial demonstrations.
• Long-horizon planning: The symbolic component handles temporal dependencies, while the neural component adapts to perceptual variations.

Results

While specific metrics are not fully detailed in the abstract, the paper reports improved success rates and reduced planning times on benchmark long-horizon tasks compared to purely neural baselines. The approach is validated on environments requiring multi-step reasoning, such as robotic manipulation and navigation.

Significance

This work contributes to the growing field of neuro-symbolic AI, offering a principled way to combine learning and reasoning. It has implications for robotics, autonomous driving, and any domain requiring sequential decision-making with sparse rewards. The abductive imitation component is particularly novel, as it relaxes the need for complete expert demonstrations.