Parallel Deep Reinforcement Learning Method for Gait Control of Biped Robot
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Bibliographic record
Abstract
In this brief, a parallel Deep Deterministic Policy Gradient (DDPG) algorithm is presented for biped robot gait control. Biped robot gait control is a high-dimensional continuous problem. It is challenging to obtain a fast and stable gait. Traditional methods cannot fully utilize autonomous exploration capability of a biped robot. A multiple Actor-Critic (AC) network is established to expand the scope of exploration and improve training efficiency. For optimizing experience replay mechanism, an experience filtering unit is introduced, and a cosine similarity method is used to classify experience. Then, a Markov Decision Process (MDP) model based on knowledge and experience is designed to solve the problem of sparse rewards. Finally, experimental results show that the parallel DDPG algorithm can make the biped robot walk more quickly and stably, and the speed reaches 0.62 m/s.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it