Reinforcement learning-based state space dimensionality reduction and optimal control strategy design in robot navigation systems
Bibliographic record
Abstract
There are many mature traditional navigation algorithms, but most of them are insufficient in the function of environment perception and understanding, and reinforcement learning can give robots the ability to learn and make decisions.This paper proposes a robot reinforcement learning navigation algorithm and optimal control strategy based on deep reinforcement learning.Firstly, Markov decision modeling for local planning of the robot navigation system is implemented, and then a POMDP belief space dimensionality reduction algorithm based on the NMF update rule is proposed to address the situation of excessive dimensionality and combined with PRM to achieve global reinforcement learning planning.Finally, considering the external information interference problem, a power controller based on the TD3 algorithm is designed to ensure that the robot navigation system can accurately track the signals even under the external interference environment.The position error of the robot under the TD3 controller tends to be close to 0, which is much lower than that of the robot under the PD controller.The experimental results of this paper show that the designed TD3 controller can effectively improve the trajectory tracking accuracy of the robot navigation system and better realize the optimization of the robot tracking control function.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 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".