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Dengwei Zhao

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4

NeurIPS Conference 2025 Conference Paper

KeeA*: Epistemic Exploratory A* Search via Knowledge Calibration

  • Dengwei Zhao
  • Shikui Tu
  • Yanan Sun
  • Lei Xu

In recent years, neural network-guided heuristic search algorithms, such as Monte-Carlo tree search and A$^\*$ search, have achieved significant advancements across diverse practical applications. Due to the challenges stemming from high state-space complexity, sparse training datasets, and incomplete environmental modeling, heuristic estimations manifest uncontrolled inherent biases towards the actual expected evaluations, thereby compromising the decision-making quality of search algorithms. Sampling exploration enhanced A$^\*$ (SeeA$^\*$) was proposed to improve the efficiency of A$^\*$ search by constructing an dynamic candidate subset through random sampling, from which the expanded node was selected. However, uniform sampling strategy utilized by SeeA$^\*$ facilitates exploration exclusively through the injection of randomness, which completely neglects the heuristic knowledge relevant to open nodes. Moreover, the theoretical support of cluster sampling remains ambiguous. Despite the existence of potential biases, heuristic estimations still encapsulate certain valuable information. In this paper, epistemic exploratory A$^\*$ search (KeeA$^\*$) is proposed to integrate heuristic knowledge for calibrating the sampling process. We first theoretically demonstrate that SeeA$^\*$ with cluster sampling outperforms uniform sampling due to the distribution-aware selection with higher variance. Building on this insight, cluster scouting and path-aware sampling are introduced in KeeA$^\*$ to further exploit heuristic knowledge to increase the sampling mean and variance, respectively, thereby generating higher-quality extreme candidates and enhancing overall decision-making performance. Finally, empirical results on retrosynthetic planning and logic synthesis demonstrate superior performance of KeeA$^*$ compared to state-of-the-art heuristic search algorithms.

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NeurIPS Conference 2024 Conference Paper

SeeA*: Efficient Exploration-Enhanced A* Search by Selective Sampling

  • Dengwei Zhao
  • Shikui Tu
  • Lei Xu

Monte-Carlo tree search (MCTS) and reinforcement learning contributed crucially to the success of AlphaGo and AlphaZero, and A$^*$ is a tree search algorithm among the most well-known ones in the classical AI literature. MCTS and A$^*$ both perform heuristic search and are mutually beneficial. Efforts have been made to the renaissance of A$^*$ from three possible aspects, two of which have been confirmed by studies in recent years, while the third is about the OPEN list that consists of open nodes of A$^*$ search, but still lacks deep investigation. This paper aims at the third, i. e. , developing the Sampling-exploration enhanced A$^*$ (SeeA$^*$) search by constructing a dynamic subset of OPEN through a selective sampling process, such that the node with the best heuristic value in this subset instead of in the OPEN is expanded. Nodes with the best heuristic values in OPEN are most probably picked into this subset, but sometimes may not be included, which enables SeeA$^*$ to explore other promising branches. Three sampling techniques are presented for comparative investigations. Moreover, under the assumption about the distribution of prediction errors, we have theoretically shown the superior efficiency of SeeA$^*$ over A$^*$ search, particularly when the accuracy of the guiding heuristic function is insufficient. Experimental results on retrosynthetic planning in organic chemistry, logic synthesis in integrated circuit design, and the classical Sokoban game empirically demonstrate the efficiency of SeeA$^*$, in comparison with the state-of-the-art heuristic search algorithms.

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NeurIPS Conference 2023 Conference Paper

Generalized Weighted Path Consistency for Mastering Atari Games

  • Dengwei Zhao
  • Shikui Tu
  • Lei Xu

Reinforcement learning with the help of neural-guided search consumes huge computational resources to achieve remarkable performance. Path consistency (PC), i. e. , $f$ values on one optimal path should be identical, was previously imposed on MCTS by PCZero to improve the learning efficiency of AlphaZero. Not only PCZero still lacks a theoretical support but also considers merely board games. In this paper, PCZero is generalized into GW-PCZero for real applications with non-zero immediate reward. A weighting mechanism is introduced to reduce the variance caused by scouting's uncertainty on the $f$ value estimation. For the first time, it is theoretically proved that neural-guided MCTS is guaranteed to find the optimal solution under the constraint of PC. Experiments are conducted on the Atari $100$k benchmark with $26$ games and GW-PCZero achieves $198\%$ mean human performance, higher than the state-of-the-art EfficientZero's $194\\%$, while consuming only $25\\%$ of the computational resources consumed by EfficientZero.

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ICML Conference 2022 Conference Paper

Efficient Learning for AlphaZero via Path Consistency

  • Dengwei Zhao
  • Shikui Tu
  • Lei Xu 0001

In recent years, deep reinforcement learning have made great breakthroughs on board games. Still, most of the works require huge computational resources for a large scale of environmental interactions or self-play for the games. This paper aims at building powerful models under a limited amount of self-plays which can be utilized by a human throughout the lifetime. We proposes a learning algorithm built on AlphaZero, with its path searching regularised by a path consistency (PC) optimality, i. e. , values on one optimal search path should be identical. Thus, the algorithm is shortly named PCZero. In implementation, historical trajectory and scouted search paths by MCTS makes a good balance between exploration and exploitation, which enhances the generalization ability effectively. PCZero obtains $94. 1%$ winning rate against the champion of Hex Computer Olympiad in 2015 on $13\times 13$ Hex, much higher than $84. 3%$ by AlphaZero. The models consume only $900K$ self-play games, about the amount humans can study in a lifetime. The improvements by PCZero have been also generalized to Othello and Gomoku. Experiments also demonstrate the efficiency of PCZero under offline learning setting.

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