Map-less Navigation Algorithm for Autonomous Vehicles Based on Deep Reinforcement Learning
Bibliographic record
Abstract
This paper focuses on the map-less navigation problem of autonomous vehicles based on deep reinforcement learning, and proposes a map-less navigation method for autonomous vehicles based on an improved Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm. Aiming at the problems of navigation success rate, exploration performance, and training time of existing map-less navigation algorithms based on deep reinforcement learning, the following innovations are used to optimize the above performance: ① Optimize the neural network structure of the TD3 algorithm to enhance the exploration ability of autonomous vehicles in complex environments. ② Construct a composite reward function to integrate dense rewards and sparse rewards, which significantly speeds up the training speed of the algorithm. Finally, the algorithm in this paper only needs 12% of the training amount of the comparison algorithm to achieve the same success rate. A comprehensive test environment and a special test environment were built in a simulation environment for comparative experiments. The results show that the navigation success rate of the algorithm in this paper is increased by 11.80% in the comprehensive test environment; the obstacle avoidance success rate is increased by 40% and 70% in the special test environment, and the exploration success rate is increased by 100%. In the test of real complex environment, the navigation algorithm is not adjusted, and it can effectively drive the autonomous vehicle to perform map-less navigation. The navigation effect and portability of the algorithm are verified.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".