Entropy-based adaptive exploit-explore coefficient for Monte-Carlo path planning
Bibliographic record
Abstract
Efficient path planning for autonomous vehicles in cluttered environments is a challenging sequential decision-making problem under uncertainty. In this context, this paper implements a partially observable stochastic shortest path (PO-SSP) planning problem for autonomous urban navigation of Unmanned Aerial Vehicles (UAVs). To solve this planning problem, the POMCP-GO algorithm is used, which is goal oriented variant of POMCP, one of the fastest online state-of-the-art solvers for partially observable environments based on Monte Carlo Planning. This algorithm relies on the Upper Confidence Bounds (UCB1) algorithm as action selection strategy. UCB1 depends on an exploration constant typically adjusted empirically. Its best value varies significantly between planning problems, and hence, an exhaustive search to find the most suitable value is required. This exhaustive search applied to a complex path planning problem may be extremely time consuming. Moreover, considering real applications where online planning is needed, this extensive search is not suitable. Thereby this paper explores the use of an adaptive exploration coefficient for action selection during planning. Monte-Carlo value backup approximation is also applied which empirically demonstrates to accelerate the policy value convergence. Simulation results show that the use of the adaptive exploration co- \nefficient within a user-defined interval achieves better convergence and success rates when compared with most hand-tuned fixed coefficients in said interval, although never achieving the same results as the best fixed coefficient. Therefore, a compromise must be made between the desired quality of the results and the time one is willing to spend on the exhaustive search for the best coefficient value before planning.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.009 | 0.012 |
| Research integrity | 0.000 | 0.001 |
| 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; both teacher heads agree on what is shown here.
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".