ExPoSe: Combining State-Based Exploration with Gradient-Based Online Search
Dixant Mittal, Siddharth Aravindan, Wee Sun Lee

TL;DR
ExPoSe is a novel online search algorithm that combines state-based exploration with gradient-based online search, effectively sharing information among states and incorporating exploration, leading to superior performance in various decision-making tasks.
Contribution
This paper introduces ExPoSe, an innovative algorithm that integrates exploration into gradient-based online search, enhancing information sharing among states for improved decision-making.
Findings
ExPoSe outperforms existing online search algorithms in Atari, Sokoban, and graph problems.
The method effectively incorporates exploration into gradient-based search.
Results show consistent improvements across diverse domains.
Abstract
Online tree-based search algorithms iteratively simulate trajectories and update action-values for a set of states stored in a tree structure. It works reasonably well in practice but fails to effectively utilise the information gathered from similar states. Depending upon the smoothness of the action-value function, one approach to overcoming this issue is through online learning, where information is interpolated among similar states; Policy Gradient Search provides a practical algorithm to achieve this. However, Policy Gradient Search lacks an explicit exploration mechanism, which is a key feature of tree-based online search algorithms. In this paper, we propose an efficient and effective online search algorithm called Exploratory Policy Gradient Search (ExPoSe), which leverages information sharing among states by updating the search policy parameters directly, while incorporating a…
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Taxonomy
TopicsSports Analytics and Performance · Artificial Intelligence in Games · Experimental Behavioral Economics Studies
