<p>Experimental settings.</p>

5.5 KB of experimental data supporting research on an improved REINFORCE algorithm for robot visual navigation. The dataset, authored by Yuanyuan Hu and last updated in May 2026, likely contains parameters and results from simulated and real-world robot navigation tests. Reported algorithm performance includes 95.7% accuracy, 91.2% recall, and a 92.5% navigation success rate in simulations.

Use Cases

• Benchmarking visual navigation algorithms based on reported accuracy and recall metrics
• Training reinforcement learning agents for autonomous driving based on the described strategic gradient integration
• Simulating robot navigation performance in intricate working conditions as described in the paper

Strengths

• Reported algorithm performance metrics are specific: 95.7% accuracy and 91.2% recall
• Real-world testing results are quantified: navigation decision time reduced by 20.3%
• Dataset is openly licensed under CC-BY-4.0

Limitations

• Row count is unknown, which may limit suitability assessment
• Column-level documentation is absent; field semantics must be inferred after download
• The dataset is very small at 5.5 KB, indicating limited scope

Provenance

Source

Yuanyuan Hu via figshare

Collection Method

Likely generated from simulated and real-world robot navigation experiments described in the associated research paper.

Freshness

Last updated 2026-05-11 17:43:30