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Online tree-based search algorithms iteratively simulate trajectories andupdate action-values for a set of states stored in a tree structure. It worksreasonably well in practice but fails to effectively utilise the informationgathered from similar states. Depending upon the smoothness of the action-valuefunction, one approach to overcoming this issue is through online learning,where information is interpolated among similar states; Policy Gradient Searchprovides a practical algorithm to achieve this. However, Policy Gradient Searchlacks an explicit exploration mechanism, which is a key feature of tree-basedonline search algorithms. In this paper, we propose an efficient and effectiveonline search algorithm called Exploratory Policy Gradient Search (ExPoSe),which leverages information sharing among states by updating the search policyparameters directly, while incorporating a well-defined exploration mechanismduring the online search process. We evaluate ExPoSe on a range ofdecision-making problems, including Atari games, Sokoban, and Hamiltonian cyclesearch in sparse graphs. The results demonstrate that ExPoSe consistentlyoutperforms other popular online search algorithms across all domains. TheExPoSe source code is available at\textit{\url{https://github.com/dixantmittal/ExPoSe}}.

ExPoSe: Combining State-Based Exploration with Gradient-Based Online Search