Work

Data-Density guided Reinforcement Learning

Year: 2025

Type: conference-paper

Abstract: This paper investigates reinforcement learning by avoiding low-density state regions using modified reward functions.The approach leverages data-density models within the state space, enabling a custom reward function that penalizes transitions into sparse regions.Applied in the Pendulum environment, this method encourages exploration in wellsampled areas while avoiding less-explored states.Empirical results show that this method effectively balances reward optimization with state confidence, enabling robust policy learning in challenging environments.

Source: European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN)

DOI: 10.14428/esann/2025.es2025-194

Authors: Leon Lantz, Maximilian Schieder, Michel Tokic

Institutions: Ludwig-Maximilians-Universität München, Siemens (Germany)

Language: English

Topic: Reinforcement Learning in Robotics

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: bronze

Funder: Bundesministerium für Bildung und Forschung

OpenAlex ID: W4409480504

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