Reinforcement Learning for Path Planning in Communication-Limited Environments
Bibliographic record
Abstract
This study presents a groundbreaking exploration of using reinforcement learning (RL) to enhance path planning in scenarios with limited communication, a significant challenge in critical fields such as underwater exploration and disaster response. It begins by highlighting the importance of efficient path planning in situations characterized by intermittent connectivity and restricted bandwidth. Traditional path planning methods are assessed for their effectiveness in achieving optimal paths while minimizing time and energy consumption. The research’s focal point is the application of RL techniques, including Q-learning, Deep Q-Networks (DQN), and Policy Gradient methods, to empower autonomous agents to learn and refine their pathfinding through trial and error. Special emphasis is placed on ensuring learning stability and convergence of these RL models despite communication challenges. The study concludes by showcasing how RL not only improves path planning in these difficult environments but also paves the way for more robust and adaptive planning approaches in operations where communication is critical.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".