Subgoal-Guided Policy Heuristic Search with Learned Subgoals

Abstract: Policy tree search is a family of tree search algorithms that use a policy to guide the search. These algorithms provide guarantees on the number of expansions required to solve a given problem that are based on the quality of the policy. While these algorithms have shown promising results, the process in which they are trained requires complete solution trajectories to train the policy. Search trajectories are obtained during a trial-and-error search process. When the training problem instances are hard, learning can be prohibitively costly, especially when starting from a randomly initialized policy. As a result, search samples are wasted in failed attempts to solve these hard instances. This paper introduces a novel method for learning subgoal-based policies for policy tree search algorithms. The subgoals and policies conditioned on subgoals are learned from the trees that the search expands while attempting to solve problems, including the search trees of failed attempts. We empirically show that our policy formulation and training method improve the sample efficiency of learning a policy and heuristic function in this online setting.

• The paper demonstrates that learned subgoals significantly improve policy tree search by using both successes and failures during training.
• It employs VQVAE to generate subgoal representations that partition complex problems into manageable segments.
• Experimental results in domains like CraftWorld and BoulderDash confirm enhanced performance with reduced node expansions.

Subgoal-Guided Policy Heuristic Search with Learned Subgoals: An Analysis

The paper under examination introduces an innovative methodology for improving policy tree search algorithms in single-agent deterministic search problems, a significant subclass of AI challenges often resembling complex "needle-in-the-haystack" scenarios. The primary contribution involves a paradigm shift in how search policies are trained and executed by leveraging learned subgoals to guide decision-making. This technique, referred to as Subgoal-Guided Policy, aims to enhance efficiency by utilizing both successful and unsuccessful search trajectories during the training phase.

Problem Context and Solution Overview

Standard policy tree search algorithms, such as Levin Tree Search (LevinTS) and Policy-Guided Heuristic Search (PHS*), utilize policy-driven mechanisms to navigate through search spaces. However, these algorithms traditionally require complete solution trajectories to refine their policies effectively, posing significant obstacles when dealing with complex problem instances where solutions are arduous to uncover from randomly initialized policies.

The proposed subgoal-guided approach introduces subgoal discovery within the learning process. Subgoals serve to partition the broader search horizon into manageable segments, guided by low-level policies conditioned on these subgoal states. Complementarily, high-level policies assess the importance of potential subgoals, integrating them into a cohesive plan.

Methodological Innovations

Key advancements of this method include:

• VQVAE Subgoal Generator: Application of Vector Quantized Variational Autoencoders (VQVAE) for subgoal generation marks a novel intersection of representation learning and search strategies. The encoder learns to encapsulate the differential transitions between states, while the decoder reconstructs subgoal states from these learned representations.
• Training from Non-Solution Data: Unlike traditional approaches that discard failed search attempts, the proposed method proactively utilizes these scenarios to gather subgoal data that guide both policy refinement and heuristic optimization. This incorporation significantly amplifies sample efficiency, reducing unnecessary computational overhead.

Experimental Evaluation

Empirical evidence showcases the robustness and efficiency of subgoal-guided policies compared to conventional methods. In easier domains, the algorithms utilizing subgoal-guidance required fewer node expansions during both the training and testing phases, confirming superior sample efficiency. When confronted with harder problem instances—a domain where classical methods typically falter—the subgoal-guided policy demonstrated remarkable proficiency in resolving all test cases.

Specifically, experiments involving arduous domains like CraftWorld and BoulderDash substantiate the method's capacity to learn policies that solve problems requiring intricate strategies within time and node expansion constraints deemed prohibitive for traditional approaches.

Implications and Future Directions

The implications of leveraging a subgoal-guided approach are multifaceted. Practically, this method enables more tractable solutions to complex single-agent search problems, making it applicable to domains like robotics and network routing. Theoretical impacts resonate with its strategic decomposition of search spaces, facilitating a model where learning from failures becomes as informative as success.

Future research trajectories can explore adaptive schemes for dynamic subgoal generation, which could further refine policy precision and solve increasingly complex partitioning in search domains. Additionally, extending this approach to multi-agent environments presents a promising venue, requiring coordination of subgoals across agents.

In conclusion, this paper elucidates a novel framework that not only advances the frontier in policy tree search algorithms but also enriches the foundation for exploiting subgoal-driven insights within AI's pursuit of efficient problem-solving strategies.