Multi-agent Motion Planning for Dense and Dynamic Environments via Deep\n Reinforcement Learning
Bibliographic record
Abstract
This paper introduces a hybrid algorithm of deep reinforcement learning (RL)\nand Force-based motion planning (FMP) to solve distributed motion planning\nproblem in dense and dynamic environments. Individually, RL and FMP algorithms\neach have their own limitations. FMP is not able to produce time-optimal paths\nand existing RL solutions are not able to produce collision-free paths in dense\nenvironments. Therefore, we first tried improving the performance of recent RL\napproaches by introducing a new reward function that not only eliminates the\nrequirement of a pre supervised learning (SL) step but also decreases the\nchance of collision in crowded environments. That improved things, but there\nwere still a lot of failure cases. So, we developed a hybrid approach to\nleverage the simpler FMP approach in stuck, simple and high-risk cases, and\ncontinue using RL for normal cases in which FMP can't produce optimal path.\nAlso, we extend GA3C-CADRL algorithm to 3D environment. Simulation results show\nthat the proposed algorithm outperforms both deep RL and FMP algorithms and\nproduces up to 50% more successful scenarios than deep RL and up to 75% less\nextra time to reach goal than FMP.\n
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".