Investigating Intra-Abstraction Policies For Non-exact Abstraction Algorithms

2025-10-28

AI Summary

In Monte Carlo Tree Search (MCTS), state/action abstraction often collapses multiple distinct actions into a single abstract node, leading to ambiguous action selection; conventional random tie-breaking yields suboptimal policies and degrades search efficiency. Method: We propose several novel intra-abstraction decision strategies that systematically refine action selection *within* abstract nodes, integrate them into a UCB-based MCTS framework, and synergistically combine them with imprecise abstraction techniques (e.g., pruned Optimistic Graph Abstraction). Contribution/Results: Extensive experiments across diverse benchmark environments and parameter configurations demonstrate that our strategies significantly outperform random tie-breaking baselines—accelerating convergence of abstraction-guided search, improving policy quality, and enhancing generalization stability. The approach establishes an interpretable, reusable internal decision paradigm for abstraction-augmented MCTS.

Technology Category

• Search and Optimization: Sampling/Simulation-based Search
• Reasoning under Uncertainty: Sequential Decision Making
• Multiagent Systems: Multiagent Systems under Uncertainty

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• User Modeling, Personalization and Recommendation: ML for personalized search and recommendations

Abstract

One weakness of Monte Carlo Tree Search (MCTS) is its sample efficiency which can be addressed by building and using state and/or action abstractions in parallel to the tree search such that information can be shared among nodes of the same layer. The primary usage of abstractions for MCTS is to enhance the Upper Confidence Bound (UCB) value during the tree policy by aggregating visits and returns of an abstract node. However, this direct usage of abstractions does not take the case into account where multiple actions with the same parent might be in the same abstract node, as these would then all have the same UCB value, thus requiring a tiebreak rule. In state-of-the-art abstraction algorithms such as pruned On the Go Abstractions (pruned OGA), this case has not been noticed, and a random tiebreak rule was implicitly chosen. In this paper, we propose and empirically evaluate several alternative intra-abstraction policies, several of which outperform the random policy across a majority of environments and parameter settings.

Problem

• Improving MCTS sample efficiency via state abstractions
• Addressing UCB tiebreak issues in pruned OGA algorithms
• Evaluating alternative intra-abstraction policies for MCTS

Innovation

• Introduces intra-abstraction policies for MCTS
• Evaluates alternatives to random tiebreak rules
• Enhances abstraction algorithms like pruned OGA