Our work “Information-guided Planning: An Online Approach for Partially Observable Problems” was presented at the 37th Conference on Neural Information Processing Systems (NeurIPS 2023). The work integrates entropy into the decision-making process of the Monte Carlo simulations of an on-line planner, improving the agent’s performance, especially in scenarios with sparse rewards. The paper is freely available. Our source code is also available in the paper’s GitHub.