Code, trained checkpoints and trial-level data for "Risk-Aware Greenhouse Robot Navigation with Deep Q-Networks and RRT*: Checkpoint Sensitivity and the Role of the Objective"
Year: 2026
Type: dataset
Abstract: This record contains the code and data supporting the article "Risk-Aware Greenhouse Robot Navigation with Deep Q-Networks and RRT*: Checkpoint Sensitivity and the Role of the Objective" (N. Karakoç, İ. Ö. Bucak). It includes the simulation framework of the geothermal tomato greenhouse, the DQN training script, the RRT* and cost-aware RRT* (RRT*-CA) planners, the unified timing harness, the final-episode and best-window checkpoints of all ten trained DQN agents with their training logs, the trial-level results, and the analysis and figure scripts used in the article.
Authors: Karakoç, Neslihan, Bucak, İhsan Ömür
Institution: Iğdır Üniversitesi
Language: English
Topic: Smart Agriculture and AI
Subfield: Plant Science
Domain: Life Sciences
Open Access status: green
OpenAlex ID: W7214760078