Helyszínrl

Helyszínrl is a term used to describe a location-aware reinforcement learning framework and accompanying dataset ecosystem. It provides standardized environments that integrate geospatial data with reinforcement learning tasks, enabling agents to learn policies that take geographic context into account. The project is designed to be interoperable with common RL libraries and geospatial data formats.

The name combines helyszín, Hungarian for "location" or "scene," with RL, signaling its focus on spatially grounded learning. The ecosystem supports synthetic environments, real-world datasets, and tools for evaluation across diverse locations, aiming to facilitate comparisons and reproducibility in location-based learning experiments.

Core components include a uniform API for defining states and actions in geographic spaces, environment wrappers for simulators, and a suite of baseline algorithms. It provides evaluation metrics such as cumulative reward, learning efficiency, and generalization across locations. Privacy-preserving data handling and reproducibility features are emphasized to support research and collaboration.

Developed by researchers and practitioners in a collaborative community, helyszínrl emerged in the early stages of intelligent geography studies. It is used in academic experiments, urban planning simulations, and robotics research to study how location context affects decision making.

Common applications include urban mobility planning, autonomous navigation, logistics optimization, and disaster response planning. See also reinforcement learning, geospatial data, and simulation environments.