Deep Reinforcement Learning With NMPC Assistance Nash Switching for Urban Autonomous Driving
Bibliographic record
Abstract
Deep Deterministic Policy Gradient (DDPG) is a promising reinforcement learning technique with the potential to resolve complicated tasks and handle high-dimensional state/action spaces. However, it suffers from sample inefficiency, requiring a high number of training samples. To speed up the training, we propose$\epsilon$-annealing and Q-learning switching methods to aid the training of DDPG with Nonlinear Model Predictive Control (NMPC) controller to solve priority calculation and merging of autonomous vehicles at roundabouts. We further expand the Q-learning switch with double replay memory and Nash Q-value updates. The performance of these switching methods are compared to DDPG and demonstrate that Nash switch outperforms other methods. To reduce conservativeness, we test training using variable traffic density. We test three selection methods inside Q-learning and show constant threshold switch has at least ten times higher mean reward for 50 episodes training. We also compare Q-learning with NMPC and PID assistance and show that NMPC has 114% higher mean reward. We compare Q-learning switch and novel Nash switch method under noise-free and noisy input conditions to prove an increase of 35% mean reward and decrease of 4% std for Nash updates. We analyze efficacy of Q-learning and Nash switch approaches w.r.t NMPC and demonstrate comparable performance between Nash switch and NMPC. We juxtapose driving results of switch Q-learning and Nash switch with DDPG algorithm to prove Nash switch strategy has higher overall performance. Finally, we compare Nash switch’s performance with DDPG for highway merging scenario which shows 159% higher mean reward.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| 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".